{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A notebook demonstrating the prediciotn of groundwater salinity onto a grid. First we determine that there is a strong linear relationship between groundwater EC (measured from pore fluid samples) and the representative bulk AEM conductivity. We use this empirical relationship to estimate the groundwater quality (EC) for the top 20m of saturated aquifer using our best estimate of bulk conductivity. Finally we investigate the performance using independent measurments of groundwater salinity\n",
    "\n",
    "Neil Symington\n",
    "neil.symington@ga.gov.au"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import rasterio\n",
    "from sqlite3 import dbapi2 as sqlite\n",
    "import numpy as np\n",
    "from hydrogeol_utils import SNMR_utils, AEM_utils, spatial_functions, borehole_utils, grid_utils\n",
    "from shapely.geometry import Polygon, shape\n",
    "import netCDF4\n",
    "import math\n",
    "import time\n",
    "import os, glob\n",
    "import gc\n",
    "from geophys_utils._netcdf_line_utils import NetCDFLineUtils\n",
    "from geophys_utils._netcdf_point_utils import NetCDFPointUtils\n",
    "from geophys_utils import points2convex_hull\n",
    "import rasterio\n",
    "from rasterio import Affine\n",
    "from rasterio.warp import reproject, Resampling\n",
    "from scipy import interpolate, stats\n",
    "import sqlalchemy as db\n",
    "from sqlalchemy import create_engine, event\n",
    "import matplotlib.pyplot as plt\n",
    "from hydrogeol_utils.db_utils import makeCon, closeCon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resample_raster2array(infile, newaff, new_shape):\n",
    "    \"\"\"\n",
    "    Function for opening a raster and resampling it onto a different grid.\n",
    "    @param infile: path to raster\n",
    "    @param: newAff: affine object for resampled raster\n",
    "    @param: new_shape: tuple: (rows, columns) for new array\n",
    "    \n",
    "    returns:\n",
    "    numpy array with resampled values\n",
    "    \"\"\"\n",
    "\n",
    "    # Open\n",
    "    src = rasterio.open(infile)\n",
    "\n",
    "    # Extract data as an array\n",
    "    arr = src.read()[0]\n",
    "\n",
    "    # Get the affine\n",
    "    aff = src.transform\n",
    "\n",
    "\n",
    "    # Create new array with the grid\n",
    "    # coordinates from the kriging\n",
    "    newarr = np.empty(shape=new_shape)\n",
    "\n",
    "    # Reproject\n",
    "    reproject(\n",
    "        arr, newarr,\n",
    "        src_transform=aff,\n",
    "        dst_transform=newaff,\n",
    "        src_crs=src.crs,\n",
    "        dst_crs=src.crs,\n",
    "        resampling=Resampling.bilinear)\n",
    "\n",
    "    src.close()\n",
    "\n",
    "    # This is a hack to handle different nulls which are always lower than -900\n",
    "    \n",
    "    newarr[newarr < -900] = np.nan\n",
    "\n",
    "    return newarr\n",
    "\n",
    "\n",
    "    \n",
    "# Function for finding the nea\n",
    "\n",
    "def get_bore(df, borehole_id):\n",
    "    \"\"\"\n",
    "    @param: df: pandas dataframe with borehole data\n",
    "    @param: borehole_id: integer with identification number of borehole\n",
    "    \n",
    "    returns\n",
    "    dataframe with only rows corresponding to particular bore\n",
    "    \"\"\"\n",
    "    mask = df['borehole_id'] == borehole_id\n",
    "    return df[mask]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Birng the AEM data into memory\n",
    "\n",
    "nc_dir = r\"C:\\Users\\PCUser\\Desktop\\EK_data\\AEM\\netCDF\"\n",
    "\n",
    "cond_path = os.path.join(nc_dir,'EastKimberley_wb_inversion.nc')\n",
    "cond_dataset = netCDF4.Dataset(cond_path, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"300\" viewBox=\"460023.87999990705 8241660.879944195 110039.11500015535 123703.7400036538\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,16607025.499892045)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"824.6916000243586\" opacity=\"0.6\" d=\"M 497703.0000000027,8246242.49994433 L 497669.00000000256,8246242.99994433 L 497635.8125000035,8246246.49994433 L 497603.40625000355,8246253.999944331 L 496786.3125000036,8246526.499944339 L 464605.5000000424,8257490.999944661 L 466221.6875000397,8304389.499946042 L 466469.6875000387,8305081.999946062 L 488233.6875000138,8330164.999946804 L 491461.4062500101,8333382.4999469 L 523230.31249997555,8360782.999947714 L 550185.3749999455,8350756.499947417 L 554398.812499941,8345955.999947274 L 565480.3749999278,8329967.999946802 L 565481.374999927,8329943.4999468 L 560532.9999999315,8318658.499946466 L 497768.9062499999,8246255.499944331 L 497736.59375000146,8246246.49994433 L 497703.0000000027,8246242.49994433 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x209db813940>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a convex hull around the Keep River area based on line 3xxx,xxx\n",
    "\n",
    "# Initialise an instance of the netCDF point and line utility classes\n",
    "cond_line_utils = NetCDFLineUtils(cond_dataset)\n",
    "cond_point_utils = NetCDFPointUtils(cond_dataset)\n",
    "\n",
    "# Display the lines for the conductivity mode\n",
    "\n",
    "lines = cond_line_utils.line\n",
    "\n",
    "# Get the utm coordinates using the mask created above\n",
    "utm_wkt, aem_coords = cond_point_utils.utm_coords(cond_point_utils.xycoords)\n",
    "\n",
    "# Get the convex hull of the subset\n",
    "convex_hull = points2convex_hull(aem_coords)\n",
    "\n",
    "\n",
    "# Create a shapely object of the convex hull\n",
    "Keep_poly = Polygon(convex_hull)\n",
    "\n",
    "Keep_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to C:\\Users\\PCUser\\Desktop\\EK_data\\Boreholes\\East_Kimberley_borehole_data.sqlite. Temporary working copy created.\n"
     ]
    }
   ],
   "source": [
    "# Extract data from the borehole spatialite database\n",
    "\n",
    "# Extract borehole data from the database\n",
    "\n",
    "DB_PATH = r\"C:\\Users\\PCUser\\Desktop\\EK_data\\Boreholes\\East_Kimberley_borehole_data.sqlite\"\n",
    "\n",
    "SPATIALITE_PATH = r'C:\\mod_spatialite-NG-win-amd64'\n",
    "\n",
    "# Add spatialite dll to path\n",
    "os.environ['PATH'] = SPATIALITE_PATH + ';' + os.environ['PATH']\n",
    "\n",
    "engine = db.create_engine('sqlite:///' + DB_PATH, module=sqlite)\n",
    "\n",
    "\n",
    "connection = makeCon(DB_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select b.* from borehole b  where within(b.geom,GeomFromText('POLYGON ((497703.0000000027 8246242.49994433, 497669.0000000026 8246242.99994433, 497635.8125000035 8246246.49994433, 497603.4062500036 8246253.999944331, 496786.3125000036 8246526.499944339, 464605.5000000424 8257490.999944661, 466221.6875000397 8304389.499946042, 466469.6875000387 8305081.999946062, 488233.6875000138 8330164.999946804, 491461.4062500101 8333382.4999469, 523230.3124999756 8360782.999947714, 550185.3749999455 8350756.499947417, 554398.812499941 8345955.999947274, 565480.3749999278 8329967.999946802, 565481.374999927 8329943.4999468, 560532.9999999315 8318658.499946466, 497768.9062499999 8246255.499944331, 497736.5937500015 8246246.49994433, 497703.0000000027 8246242.49994433))'));\n"
     ]
    }
   ],
   "source": [
    "# Extract boreholes from within this geometry\n",
    "\n",
    "df_header = borehole_utils.extract_boreholes_within_geometry('borehole', connection,\n",
    "                                                             Keep_poly.wkt, columns = 'all',\n",
    "                                                             verbose = True)\n",
    "# Keep only bores with both EC\n",
    "\n",
    "mask = df_header['Induction_acquired'] == 1\n",
    "\n",
    "df_header= df_header[mask]\n",
    "\n",
    "# Get the enos\n",
    "\n",
    "enos= df_header['borehole_id'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "select t.Depth, t.EC, t.borehole_id from pore_fluid_EC_pH t where t.borehole_id in (626981,626984,626986,626987,626988,626989,626990,626991,626992,635728,635729,635730,635732,635733,635734,635735,635736,635737,635738,635739,635740,635741,635742,635743,635744,635745,635746,635921,635957,635958,635959,635960,636181,636182,636183,636184,636185,636186,636189,636190,636191,636193,636194,636195,636196,636197,636198,636200,636201,636204);\n"
     ]
    }
   ],
   "source": [
    "# First import all the datasets based on our enos (primary key)\n",
    "\n",
    "df_EC = borehole_utils.extract_sql_with_primary_key(\"pore_fluid_EC_pH\", \n",
    "                                                       ['Depth', 'EC', 'borehole_id'],\n",
    "                                                       connection, enos, verbose = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get bulk the representative AEM conductivity profiles using a nearest neighboour search\n",
    "\n",
    "cond_point_utils = NetCDFPointUtils(cond_dataset)\n",
    "\n",
    "utm_wkt, aem_coords = cond_point_utils.utm_coords(cond_point_utils.xycoords)\n",
    "\n",
    "# Extract the AEM conductivity using nearest neighbour\n",
    "distances, indices = spatial_functions.nearest_neighbours(df_header[['Easting','Northing']],\n",
    "                                                          aem_coords,\n",
    "                                                          points_required = 1,# return 1 closest point\n",
    "                                                          max_distance = 250.)\n",
    "# Remove nulls which are >250m from a FID\n",
    "mask = np.isfinite(distances)\n",
    "\n",
    "indices = indices[mask]\n",
    "df_header = df_header[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract variables as arrays using the index mask\n",
    "conductivity_profile = cond_dataset['conductivity'][indices]\n",
    "\n",
    "depth_tops = cond_dataset['layer_top_depth'][indices]\n",
    "\n",
    "depth_bottom = np.nan*np.ones(depth_tops.shape, dtype = np.float32)\n",
    "\n",
    "depth_bottom[:,:-1] = depth_tops[:,1:]\n",
    "\n",
    "east = cond_dataset['easting'][indices]\n",
    "north = cond_dataset['northing'][indices]\n",
    "doi = cond_dataset['depth_of_investigation'][indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an AEM dataframe\n",
    "\n",
    "df_AEM = pd.DataFrame(columns = ['Depth_from', 'Depth_to',\n",
    "                                 'conductivity', 'easting',\n",
    "                                 'northing', 'borehole_id', 'EC',\n",
    "                                 'doi'])\n",
    "\n",
    "df_AEM['conductivity'] = conductivity_profile.flatten()\n",
    "df_AEM['Depth_from'] = depth_tops.flatten()\n",
    "df_AEM['Depth_to'] = depth_bottom.flatten()\n",
    "\n",
    "df_AEM['easting'] = np.repeat(east, conductivity_profile.shape[1])\n",
    "df_AEM['northing'] = np.repeat(north, conductivity_profile.shape[1])\n",
    "\n",
    "df_AEM['doi'] = np.repeat(doi, conductivity_profile.shape[1])\n",
    "\n",
    "df_AEM['borehole_id'] = np.repeat(df_header['borehole_id'].values,\n",
    "                                   conductivity_profile.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mask below the doi\n",
    "\n",
    "mask = df_AEM['doi'] > df_AEM['Depth_from']\n",
    "\n",
    "df_AEM = df_AEM[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PCUser\\Anaconda3\\envs\\hydrogeol_utils\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "# Now we iterate through each AEM layer and find the median EC\n",
    "\n",
    "for bore in df_AEM['borehole_id'].unique():\n",
    "    \n",
    "    df_EC_subset = get_bore(df_EC, bore)\n",
    "    \n",
    "    df_AEM_subset = get_bore(df_AEM, bore)\n",
    "    \n",
    "    new_intervals = df_AEM_subset[['Depth_from', 'Depth_to']]\n",
    "    \n",
    "    # interpolate\n",
    "    df_newEC = spatial_functions.interpolate_depths_to_intervals(df_EC_subset, ['EC'],\n",
    "                                                                 new_intervals.values,\n",
    "                                                                depth_column='Depth', how='median')\n",
    "\n",
    "    df_AEM_subset['EC'] = df_newEC['EC'].values\n",
    "    \n",
    "    df_AEM.at[df_AEM_subset.index, :] = df_AEM_subset\n",
    "    \n",
    "    \n",
    "df_AEM.dropna(how = 'any', subset = ['EC'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear regression function\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(np.log10(df_AEM['conductivity'].values),\n",
    "                                                               np.log10(df_AEM['EC'].astype(np.float).values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared =  0.8730838897685248\n",
      "EC =  0.9113338790415202  * sigma +  0.5156731346597092\n"
     ]
    },
    {
     "data": {
      "image/png": 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PFwN+EekQ6mCq+oaqpqtqeseOHaMQnjHGixemDiTF76u0LcXv4+WMwXz3oSGkpaYgQLuWflJT/AjOsNcFjwxlwdyhdEqB8f7DTG1xgHKSWFwykNxWA/mXh4aFbUYKV2ZTmgQXT5HuKL4X4TUF7q9j2ZuB/u6KeXnAY8DjwTuISBfglKqqiIzGqdjO1bFcY0wUVTfcNHj+woJl+7lYeH296/wTh7lfd9DCF2BHoAs7SrvR3O/nu9UMV7UhrvEVdsKdiDyqqn8Ukb4VHc5RL9xpTvo+zvDYn6vqd0TkCwCq+pqIfAX4IlAKFAJfVdUPqjuuTbgzpv5YmJNXaV0JgBQCjGtxjB5JFzhX3pL3S3pzXq+vOJeWmsKGF+t6LWpqItKEu0gVxTZVHVHxO6YRRplVFMZUlqiFd26cBa3c4jvHaP9xfJSzvbQbu0s7V0riB40vM2tDUNuZ2edEZA3QR0Qyq76oqhnRCtAYEzuJnHMQPIy1tRRzl/8oab5LnCxrzYZAby5pcsj3Wad0/RKpopgBjMBZAjVSf4Uxph4LN+fglaw9Ma8onOGqyq2+04z05wHwYUlP9pV1BITUFD9XS0orTb7z+8Q6peuZsBWFqpYAG0XkLlU9E8eYjDFRFG5uwYWCAAtz8mJaWdzSRulftJ/OvivklrXhg0AvrmoLwBmlNHNoV9766HjlN9Vytl5TX9c6lsIOj61glYQxDVukZpwFy/bHpMyysjLWr1/P3WU5pCYVsa6kDytK+l+rJNq19PPdh4awZt+Za/MoKgTKtcZxVTSv5eUXolxvXluYkxetj9SkVVtRGGMatkjNOHn5hYx7dTV9XlzEuFdXR+WL9bPPPuNnP/sZq1ev5tZBgxg+9VGKbuqOIKSlpvD9ecPI+eYU5gxPi9oM60gpPUzd1SbXkzGmAZkzPO2G4akVBK7NcK5JJ3eoZp4Zt3fivffe44MPPqBVq1Y8+uij3HrrrQDMHRsyoULUZlhbSo/YqvaOQkQGiMgqEdntPr9DRP459qEZY6Ll5YzBN8xkDpXh1ctVeKhmnu/95QO+94P/YcOGDQwdOpQvfelL1yqJSKI1wzpcxWKjp6LDS9PTT4GXgACAqu7EmUVtjGkg5gxPq5ROIy01JWyfcXVX4cHNPM0o407/USY120v+1SKefPJJZs+eXSnTa03jqs1Kc5bSI7a8ND21VNWPRCrl8CuNUTzGmBipuljPuFdX16rZp6IiSXOT+LWSEvaUdiInkMarffvWOa7asJQeseWlojgrIv1w71JF5BHgs5hGZYwJO9wz0jBQL0NEK/YJtcCQl6vwXm2b0aPgALc0O0d+eTKLSgZxprw1aQlu5rFV62LHS0XxZeANYJCI5OEsS/q5mEZlTBMXbjb1lqPneXtrXshZ1kC1M7CrHjd4oZm0aiqj2cO6sXfvXiaznRJfMdsDXdlR2pVykurczGNzIOq3sLmeAEQkCXjETQ7YCkhS1ctxi66WLNeTaejCNQv5RCgL8X+24mo+3FrVPhHmj+nBmn1nQu4TnIRvYU4eL/xpR6X5DW2SAjzbK59LJ4/StWtXbhpwJz/aeDYqX+w35oNy7mxq01dhaq9WSQGD3rxOVe+JSWQxYhWFaej6vLioRhOUK3oQ67IEqQBtU/xVhtEq/X3nGOUm8TvUrDePPHAfD47sEe4wNRauUrQMsvFV26SAFVaIyNeAt4CrFRtV9XyU4jPGVBFufkG4O4pu1dxReKFQqZJwkvgdIc13+XoSv6Jkti3cgyQ5Ayaj0VxkcyDqPy8VxXPu7y8HbVOg5sMbjDGevDB1YMjmmIdHplXqo6jYXtE/UPU9oaT4fRH3ETeJ3wh/Hgp8UNKT/W4SP3DmWrycuafSMqd1yUhry5rWf15yPfUJ8WOVhDExFG5+wb/OGRJ23kHwe8LxiVR6f1VtpZDpLfYxpvlxTpbfxMLi29lf1omqS9znFwailjLD5kDUf176KPw4q8xV9FOsBV5X1RvzAdQT1kdhGppoj/r554W7+O3GYzdsf+LOnvzrnCHXnlf0DyRRzpBmJxna7DMC+NgU6MGhsvZUrSCqU9sFh2zUU+LVtY/iJ4Af+LH7/El3219FJzxjmrZoLSxU9ct2XL/2bDx0gTLVa6OegisJcK7m//0vHzIq6RDtkwo5VNqeTYEeFOEPW06K30eyP4kLBTdeK9a2ucjmQNRvXiqKUao6NOj5ahHZEY3CRWQa8AOcNbN/pqqvVnld3NenAwXAM6q6LRplG1NfRMp86vXLM1Rlc/5qCd97dGjYYwQCAVqd3cvUZnspws+q4lu41KIjKX4oLghcu7IHeCVrz7WKoUWzJGbc0TViX4lpXLxUFGUi0k9VPwUQkb5A5N4yD0TEB/wPMBnIBTaLSKaq7g3a7QGgv/szBudOZkxdyzamPqntqJ/gO4ikEKOhgvsMqjbrDGsXICsri/PnzzNixAgmT55McnLytWPmV7lbKAqUX3ucXxjg7a15PDwyjTX7zlhzURPgpaJ4AVgjIodwmiB7Ac9GoezRwEFVPQQgIm8Cs4HgimI28Gt1OlI2ikiqiHRVVUshYhqNmoz6CZd+I9SQWbjejFVx5X86/wp/XpjJjqTTtGvXjqeeeoo+ffpcO3aoJrBkf1LIO541+87YPIcmotqKQlVXiUh/YCBORbFPVYujUHYaELwGYi433i2E2icNyzVlGpFwQ2GrNuMszMnjhT/vuLa+tJfJdT6Ra8ftnpTPXf6jpEiAI740/u8Xn8bvv94XEa4JLNxQWpvn0HRUW1GISDLwJWA8zt/mehF5TVWL6lh2qOEUVf/2vezj7CjyPPA8QM+ePesWmTFx5DXz6StZe65VEl74fUKgTGlBgDH+4/Rrdp4L5cmsLu7HuaLWlSoJqPkXv81zaDq8ND39GrgM/NB9Ph/4DTC3jmXnAsF5ALoDJ2qxDwCq+gZO8kLS09PrksnAmLjzMuon1CijSFo199Gv2QUGBD7FTxk5ga7sdJP4hZprEa4JLBTruG5avCxcNFBVP6+qa9yf54EBUSh7M9BfRPqISHOcxZAyq+yTCTwljjuBi9Y/YUz1WlLCyLJ93FG6n6u0IKv4VraXpkXM9Bpq4lsotV1cyDRcXu4ockTkTlXdCCAiY4ANdS1YVUtF5CvAMpzhsT9X1T0i8gX39deAxThDYw/iDI+NRie6MQ1S6g0J+0JRBvjOMsqfi0+UKVOmcLJFd7YuP0B+NaOTgpvAwt1ZWKK+psnLzOyPcTqyK6Z59gQ+BsoBVdU7YhphLdjMbNMYhUr/7U8S5o3uwdtb82hWepVx/qN09V3mVHkb7p08jXnjq1+3OlxZlvq7aanrzOxpUY7HGFML4Tq9M4Z2JenMATRvL+UIHwZ6kz5yeK0riUhlWSXRNFV7R9EQ2R2FSZR45yw6ffo0v/rDnynIP8OxsrZ8WNKLAprb1b+psbreURhjPIhWziYvysrKWL9+PevXr6eoPIkPSvpyuKwdwanAa5ICxJhIrKIwjU6iMpHWJGdTTWMM3n9Qm1ImJB+l6PIFUtP68duDrSkOkcTPJsSZaPEyPBYAEWkjIu0rfmIZlDG1VXFVn5dfiHL9qn5hTl7My/aasylUjH//1naGvbI8ZJz/vHAX//DWdk7mXyG92XFGl2zn/KUrlPYZy2tHO4SsJACSROLyuU3j52Vm9t8A3wYKuT4r2la4M/VSNDKx1la4CWuKs+5DxdyFf/zjjpC5mfILA5Waqhbm5PFy5h7yCwN0SbrEOP9R2iQVs6+0I1sC3Sn/uDRsjidw8j/FqunLNC1emp6+BgxW1bOxDsaYukrk+suhcjZVyMsv5IU/7wANn8APKmd8fekvuygNFHOXP5eBzc5yqbwFi4sHcqr8JmdnDwNRrK/CRIOXiuJTnMluxtR7iVx/uboJa17zNJ3IL2TBsv10KDvH2OSjpBBgV6AzOaXdKOP6zGlfiNTi4Y5nTF146aN4CfhARF4Xkf+u+Il1YMbURqLXX54zPI0NL95fwwVEK+vd1sctV/cwqcVBirUZ2cW3sqW0R6VKQoD5Y3p4SrlhyftMXXm5o3gdWA3swpmNbUy9VV8mitUkwd51yqDm+Ywnl0CzErYFurGrtAvlVa7nBPicu/Z1eq/21z5raks/V4pKK83ctuR9Jhq8VBSlqvrVmEdiTJTUh/WXQ/VX+H0Cyg0pOFonN6Ok8Cr3tcylY/l5unbqTqtbxvDHlbmUh1hMsm2Kn/RezsDDqp81UUODTePmpaJY4671kAVcW7BIVc/HLCpjGrhwdzZVt31tygB6lJ9kxYqtqCr3T57K6NGjSUpKIrlNu5Cr2VUdHVW1XKsYTLR5SQp4OMRmVdV6OzzWUniYhuDcuXNkZWVx9OhR+vTpw6xZs2jXrt0N+417dXXIZizL5GqiqU4pPFS1T/RDMqbpKi8v58MPP2Tt2rX4fD4yMjIYNmwYIqG7wGMx5NeaqExNeJlw5we+CNzjbloLvK6qNVtuyxjDyZMn+fWbb1N48SzHylI5nDKAQXRieJhKAqI/5DeeOalM4+Clj+IngB/4sfv8SXfbX8UqKGPiLdZX2KWlpaxbt471779PUbmPD0v6cqS8HZSUV/slHapjvC6jmSLNXq943e40TDAvfRQ7VHVoddvqE+ujMF4Fp8kIFs003cePHyczM5OzZ89yIqkTa692o7jKNVpqih+R6+tip6b4eTlj8LXyo1mR9XlxEeH+16f4fbZYURNV1zTjZSLST1U/dQ/WF0KM2TOmgQm1iluFmqa+CPVFPn1wR1avXs2mTZto06YNjz/+OFP+95OQX9JVK6r8wgAv/GkHcH0kU7S+rMM1ZflEEpYny9RvXiqKF3CGyB7CmevTC1u72jQCoZpggnntLA7V5v/9d97n45W5lBRcYdSoUUycOJEWLVrQLfW454l4gXKNyZd0uKascOfCUoCYiBWFiCThZI3tj7NutgD7VLU40vuq46YpfwvoDRwBHlXVCyH2OwJcxrmDKQ13W2RMbVT3Bei1szi4wmlOKaP8uQzwneVCYQpffuYZevXqdW3fSIkDaxNjbYSb4xEuR5WlADERKwpVLReR76nqWGBnFMt9EVilqq+KyIvu86+H2fc+y1xrYiFSmo2adBZXfJn3TLrA2ObHSCbAzkAXdpR249+DKgmoPnFgqBhjIVxTVjQ7zU3j4SUp4HIReVjCDfKundnAr9zHvwLmRPHYxngSKoEgQLuW/hp14PZu62OC/1MmtviUQjeJ39bS7nRObRVyf6+JA/1JEtcv6TnD0/juQ0NIS01BcCb0WUe2AW99FF8FWgGlIlKE0/ykqtqmDuV2VtXPcA70mYh0CrOf4lRUijN3441wB3TTjDwP0LNnzzqEZpqKuiYQVFV27tzJRN1Oia+ErYE0dpV2RknydCUe6Y6m6qineLEUICaUsMNjRWScqm4QkWRVLarxgUVWAl1CvPQN4Feqmhq07wVVvSF3gYh0U9UTbkWyAvg/qrquurJteKypCy9DUS9evEh2djYHDx6kR48etLxlDD/84FSNKpxQo65sOKpJlNoOj/1vYCTwATCipoWq6qQIAZ0Ska7u3URX4HSYY5xwf58WkXeA0UC1FYUxtVXdrGVVZfPmzaxatQpVZdq0aYwePRoR4bF7Bt9wrEgVTn1JiW5MdSJVFAER+QXQPdRCRar6t3UoNxN4GnjV/f1u1R1EpBWQpKqX3cdTcNbuNiZmIs1aHt+jBVlZWRw7doy+ffsya9YsUlNTQx7Ha5oMa+oxDUGkimImMAm4H9ga5XJfBf4oIp8HjgFzwWlqAn6mqtOBzsA7bh96M+D3qro0ynEYU0mo4ahCOTdfOcRrr32A3+9n9uzZDB06NGwSP4hc4VjFYBqasBWFOyT1TRH5WFV3RLNQVT0HTAyx/QQw3X18CKi3aUJM41S1g7m9FDC++RFuTipgwIBbmT59Oq1bt672OOE6qW3ymmmIvKQZj2olYUysRCMfUsWEuJJAgKHNTjCk2UmK8dMrfSKPzhjvOY7ghYaC2eQ10xB5GR5rTL0XrdTZc4ancfXcSXZ8sIpWWkheUmemTJnMI2P6eT7GgmX7Q1YSAjZ5zTRIVlGYRiEafQIlJZcp+eIAACAASURBVCWsWrWKgx99RLe2bZk162H69fNeQVQI17yk2HoPpmEKW1GIyFcjvVFV/zP64RhTO3VdBe7gwYNkZ2dz8eJFRo8ezcSJE2nevHmtYgk3kS7Nmp1MAxXpjuIm9/dAYBTOkFaAWdhcBlPP1HYVuMLCQpYtW8aOHTvo0KEDzz77bJ1n9kd7oSFjEi3SqKdXAERkOTBCVS+7z18G/hSX6IzxqDZfznv37mXx4sUUFBRw9913c88999CsWd1bY20inWlsvPyv6AmUBD0vwUkPbky9UZMv58uXL7NkyRI+/vhjunTpwhNPPEGXLqGyzYTnZdZ1bSqGWC/JakxteKkofgN85KbQUOBB4NcxjcqYWqjuy1lV2bFjB8uWLSMQCDBx4kTuuusukpK8JFG+LlojrOJ1XGPqyss8iu+IyBLgbnfTs6qaE9uwjImu/Px8srKyOHToED179mTWrFl06NChVseK1axrm81t6iuvDbItgUuq+gsR6SgifVT1cCwDMyYaysvLryXxExGmT59Oenp6xPQb1anrCKt4H9eYuqq2ohCRbwHpOKOffgH4gd8C42IbmjF1c+bMGbKysjh+/Di33HILM2fOpG3btnU+bmpLPxcKAjdsr+us69qO3DIm1rzcUTwIDAe2gZOPSURuivwWYxKnrKyMDRs2sG7dOpo3b86cOXO444476nQXUWFhTh5Xikpv2O731X01OhtWa+orLxVFiaqqu8pcRfpvY+qlzz77jHfffZdTp04xePBgpk2b5imJn1cLlu0nUH5jgo5WzZuF7EeoySgmG1Zr6isvFcUfReR1IFVE/hp4DvhpbMMypmYCgQDvvfceH3zwAa1atWLevHkMGjQo6uWE6y+4WHhjU1RtRjHZ+hSmPvIy6uk/RGQycAmnn+Kbqroi5pEZ49HRo0fJzMzk/PnzDB8+nMmTJ5OSEpt2/Zr0I9goJtNYRKwoRMQHLHOXNbXKwdQrxcXFrFy5ki1btpCamsqTTz5J3759Y1pmTfoRbBSTaSwiVhSqWiYiBSLSVlUvxisoY6pz4MABsrOzuXTpEmPGjOH++++vdRK/mqhJP4KNYjKNhZc+iiJgl4isAK5WbKzjmtnG1EpBQQHLli1j586ddOzYkc9//vN07949rjF47UewUUymsfBSUSxyf6JGROYCLwO3AqNVdUuY/aYBPwB8OGtpvxrNOEzDoarXkvgVFRVxzz33cPfdd0cliV+s2Cgm01iIaqi1uGJcqMitQDnwOvC1UBWF2z/yCTAZyAU2A/NVdW91x09PT9ctW0LWPaYBunz5MosWLWL//v107dqV2bNn07lz56gd3xLxGQMislVV00O95mVm9mFCLP+rqrXuNVTVj91jR9ptNHBQVQ+5+74JzAaqrShM46Cq5OTksHz5csrKypg0aRJjx46tcRK/SCwRnzHV83LfHlzDJANzgfaxCaeSNOB40PNcYEy4nUXkeeB5oM4Lz5jEu3DhAllZWRw+fJhevXoxa9Ysbr755qiXY0NYjamel3kU56ps+r6IvA98M9L7RGQlECrJ/zdU9V0PsYW63QjbTqaqbwBvgNP05OH4ph4qLy/no48+YvXq1YgIM2bMYOTIkVFJvxFKdUNYrVnKGG9NTyOCnibh3GFUm+vJnXtRF7lAj6Dn3YETdTymqcdOnz5NZmYmeXl59O/fn5kzZ9KmTZuYlhlpCKs1Sxnj8NL09L2gx6XAEeDRmERT2Wagv4j0AfKAx4DH41CuibOysjLef/991q1bR4sWLXjooYe4/fbbY3YXESzSEFZrljLG4aXp6b5oFyoiDwI/BDoCi0Rku6pOFZFuOMNgp6tqqYh8BViGMzz256q6J9qxmMTKy8sjMzOT06dPc/vttzNt2jRatYpf3slIQ1j/4a3tId9jM6tNU+Ol6envcNahuIyTDHAE8KKqLq9toar6DvBOiO0ngOlBzxcDi2tbjqm/AoEAa9asYePGjbRu3ZrHHnuMgQMTMxEt3AQ6m1ltjMNL09NzqvoDEZkKdAKexak4al1RmKbtyJEjZGVlcf78eUaMGMHkyZNJTk5OdFg3sJnVxji8VBQVDcXTgV+o6g6JR+OxaXSKiopYuXIlW7dupV27djz11FP06dMn0WGFZTOrjXF4qSi2ishyoA/wkru6XXlswzKNzSeffEJ2djZXrlxh7Nix3Hffffj9/kSHVS1bH8IYbxXF54FhwCFVLRCRm3Gan4yp1tWrV1m2bBm7du2iU6dOzJs3j7Q0++I1piHxMuqp3E3jMUBE6l9DsqmXVJXdu3ezdOlSioqKuPfee7n77rvx+XyJDs0YU0NeRj39FfB3OBPetgN3Ah8C98c2NNNQXbp0iUWLFvHJJ5+QlpZGRkYGnTp1SnRYxpha8tL09HfAKGCjqt4nIoOAV2IblmmIVJVt27axYsUKysrKmDJlCmPGjIlqEj9jTPx5WrhIVYtEBBFpoar7RMTGB5pKzp8/T1ZWFkeOHKF3797MmjWL9u3jkTvSGBNrXiqKXBFJBRYCK0TkApZzybjKy8vZuHEja9aswefzMWvWLIYPHx6X9Bt1Ycn+jPHOS2f2g+7Dl0VkDdAWWBrTqEyDcOrUKTIzMzlx4gQDBgxgxowZMU/iFw2W7M+YmvG0jqSIjAf6q+ovRKQjzloRh2Mamam3SktLWb9+Pe+//z7Jyck8/PDDDB48uN7fRVSwZH/G1IyXUU/fwkktPhAndYcf+C0wLrahmfooNzeXzMxMzpw5w5AhQ5g2bRotW7ZMdFg1Ut0aFMaYyrzcUTwIDAe2gZO4z52dbZqQkpKSa0n82rRpw/z58xkwYECiw6oVS/ZnTM14qShKVFVFRAFEJH45oE29cPjwYbKysrhw4QLp6elMmjSJFi1aJDqsWvOS7M86u425TlQjrxoqIl8D+gOTge8CzwG/V9Ufxj682klPT9ctW7YkOowGr6ioiOXLl5OTk0P79u2ZNWsWvXv3TnRYURGpIqja2Q3gTxJaJzcjvyBgFYdplERkq6qmh3ytuorCPcBkYApOJtllqroiuiFGl1UUdbdv3z4WLVrE1atXGTt2LBMmTGgQSfyiYdyrq0M2TQVL8fv47kNDrLIwjUakisLTqCdVXSEimyr2F5H2qno+ijE2GdVdySa6uePq1assWbKEPXv20KlTJ+bPn0+3bt3iGkOieenUtlFSpinxMurpb4BvA4U46cUFUKBvbENrfCKN3wdueO3v39rOy5l7eDljcMy/kFSVXbt2sXTpUkpKSrjvvvsYN25ck0ziF66zuyobJWWaCi93FF8DBqvq2WgVKiJzgZeBW4HRqhqynUhEjuAswVoGlIa7LWooIo3fr3hcVX5hoFJlEos7josXL7Jo0SIOHDhA9+7dycjIoGPHjnU+bkMVqrM7FBslZZoKLxXFp0BBlMvdDTwEvO5h3/uiWUklUm3H7xcGyng5cw/FpeVRnU2sqmzZsoWVK1eiqkydOpXRo0c3+SR+VVe2a5vi52pJKYGy6/15tiSqaUq8VBQvAR+4fRTFFRtV9W9rW6iqfgw0mJm80VLd+P1IzR35hYEbttWlnfzcuXNkZWVx9OhR+vbty8yZM2nXrl2Nj9NYVV3Zrj70HxmTKF4qiteB1cAu4r8EqgLL3Tkcr6vqG+F2FJHngecBevbsGafwaqa68ftemjuq8tpOXvFF91l+AWNbn2OgHqe5vxkZGRkMGzasyVXaNWVLopqmzEtFUaqqX63pgUVkJdAlxEvfUNV3PR5mnDsTvBNO5tp9qrou1I5uJfIGOMNjaxpvPFRt0gh1ZfpK1h4uFFS+e0jx+0j2J92wHby1k1d0oieXXmZGiyN0KCvgeHk7pkyaxvDhDXN2tTEmfrxUFGvcq/UsKjc9RRweq6qT6hgbqnrC/X1aRN4BRgMhK4qGItKVacVroZo54MY7Dq/t5P+x9GMG6THuaHGSYnysLu7L0fJ27H0vl7ljraIwxkTmpaJ43P39UtC2mA+PdVOFJKnqZffxFJxhuvVeXduzI1UmNT3u8ePHGVW0lVR/EQdLb+ajQA+K3X/2ug7vtHZ7Y5oGL+tR9Il2oSLyIPBDoCOwSES2q+pUEekG/ExVpwOdgXfctvNmOGlD6v06GLFc66Am7eQlJSWsXr2aTZs20SKpBcuL+5NX3rbSPnUZ3mlrOhjTdHiamR1tqvoO8E6I7SeA6e7jQ8DQOIdWZ/VhrYNPP/2U7Oxs8vPzGTVqFIUdb+XdzP1QXvNmq3Dqw+c0xsRHQiqKxixRax0szMnjv5bupkfBAfo3O0eLVm155pln6NWrFwC+Zs0rzQsQgX94azsLlu2vVZORrelgTNPRtGdWxUC45pxYzuJdmJPHj955jzuLt9LPd46dgS785uIAcs5fvw6YMzyNDS/ez3/NG0ZxaTkXCgIo15uMFubk1ajMRHxOY0xihK0oRGSqiDwSYvvn3GyyJoQXpg4kxV85P1IsZ/FeuXKFZdkLudt3gEJtRnbxrWwt7c7VgF5LDRKsujQiXsX7cxpjEidS09MrwKwQ21fh9C/U61TjieJlrkQ0qCo7d+5k6dKldCwrZktpGrtLO6NBdX9FM1Dw6KRwE0xq2mQUr89pjEm8SBVFS1U9U3Wjqp60Ve6uCzdENJZfmPn5+WRnZ/Ppp5/So0cPVp7sxIGiG2dWd0tNCbkITyi1aTKy2crGNA2RKopkEWmmqqXBG0XED1hDNPEfIqqqbN68mZUrV1JWrnzcrB+//CSVtinN8ftCJ60L1dRUlTUZGWMiidSZ/Rfgp8F3D+7j19zXmrxotfd7cfbsWX7xi1+wZMkSWqR24t3i29l4uR2KOAkDFdq19CNAWmrKtdXXIjUpVd3XGGNCiXRH8c/AvwJHReSou60n8L/A/xfrwBqCeAwRLSsr44MPPuC9997D7/cze/ZsvrzkHGcDRZX2C5QrLZs3I+ebUyptD5exNi01hQ0v3h+1OI0xjVfYisJtcnpRRF4BbnE3H1RVGyjvqi5teF199tlnZGZmcvLkSW677TYeeOABWrduzYk3F4XcP1QFVV3GWmOMqU6k4bH/BOBWDINUdVdFJSEi/y9O8dVrsRoiWlpayqpVq/jpT3/K5cuXefTRR5k7dy6tW7cGajaHYc7wNL770BDSUlOsqckYUyuiGnrApIhsU9URVR+Hel7fpKen65YtIVdXjbpoJ8Y7duwYmZmZnDt3jmHDhjFlyhRSUipXAKFGMqX4fVYBGGNqTUS2hltuOlIfhYR5HOp5kxWtIaLFxcWsWrWKzZs307ZtW5544gn69esXtkywOQzGmPiIVFFomMehnps6OHjwINnZ2Vy8eJHRo0czceJEmjdvHvE9NofBGBMvkSqKoSJyCefuIcV9jPs8OeaRNQGFhYUsW7aMHTt20KFDB5599tl6u4yrMabpijTqyRfuNVN3e/fuZfHixRQWFnL33Xdzzz330KyZJfM1xtQ/9s0UZ5cvX2bx4sXs27ePrl278sQTT9ClS6ilxePHVqozxkRiFUWcqCrbt29n+fLlBAIBJk6cyF133UVSUmIzvdtKdcaY6lhFEQcXLlwgOzubQ4cO0bNnTzIyMrj55psTHRZgK9UZY6qXkIpCRBbgpDAvAT4FnlXV/BD7TQN+APhw1tJ+Na6B1lF5eTmbN29m1apViAjTp08nPT0ddx3wesFWqjPGVKfaikJELnPjcNiLwBbgH921rWtqBfCSqpaKyL8BLwFfr1KuD/gfYDKQC2wWkUxV3VuL8uLuzJkzZGZmkpubyy233MLMmTNp27ZtosO6QazTkBhjGj4vdxT/CZwAfo8zNPYxoAuwH/g5MKGmharq8qCnG4EbVtIDRuPkljoEICJvArOBel1RlJWVsWHDBtatW0fz5s158MEHGTJkSL26iwh236CO/G7jsUpXApYLyhgTzEtFMU1VxwQ9f0NENqrqt0Xk/0YhhueAt0JsTwOOBz3PBcaE2A8AEXkeeB5I2FyEEydOkJmZyalTpxg8eDAPPPAArVrV3zWeFubk8fbWvEqVhAAPj7TJfMaY67xUFOUi8ijwZ/d58NV/2BnaIrIS586jqm+o6rvuPt8ASoHfhTpEiG1hy1PVN4A3wMn1FG6/WAgEAqxdu5YPP/yQVq1aMW/ePAYNGhTPEGolVEe2Amv23bCwoTGmCfNSUXwOp0P5x+7zD4EnRCQF+Eq4N6nqpEgHFZGngZnARA2dmTAX6BH0vDtOE1i9cvToUTIzMzl//jzDhw9nypQpJCc3jInr1pFtjPGi2orC7SOYFebl92tTqDua6evAvapaEGa3zUB/EekD5OH0jTxem/Jiobi4mJUrV7JlyxZSU1N58skn6du3b6LDqhHryDbGeFHtbC8R6S4i74jIaRE5JSJvi0j3Opb7I+AmYIWIbBeR19yyuonIYri2cNJXgGXAx8AfVXVPHcuNigMHDvDjH/+YLVu2cOedd/LFL36xwVUSELv1NIwxjYuXpqdf4Ix4mus+f8LdNrm2harqLWG2nwCmBz1fDCyubTnRVlBQwLJly9i5cycdO3bk85//PN2717XOTBxLV26M8SLswkXXdhDZrqrDqttWn0R74SJVZc+ePSxZsoSioiLGjx/P3XffbUn8jDGNRm0XLqpwVkSeAP7gPp8PnItWcPXd5cuXWbRoEfv376dbt25kZGTQuXPnRIdljDFx46WieA6nT+G/cEZPfgA8G8ug6gNVJScnh+XLl1NWVsbkyZO58847E57Ezxhj4s3LqKdjQEbwNhH5e+D7sQoq0S5cuEBWVhaHDx+mV69eZGRk0L59+0SHZYwxCVHbRvav0ggrivLycjZt2sTq1atJSkpixowZjBw5st6m3zDGmHiobUXR6L45CwsL+d3vfkdeXh79+/dn5syZtGnTJtFhGWNMwtW2oohriox4SE5Opn379owZM4bbb7/d7iKMMcYVtqIIk14cnLuJRjd1V0R46KGHEh2GMcbUO2ErClW9KZ6BGGOMqZ9srKcxxpiIrKIwxhgTkVUUxhhjIrKKwhhjTERWURhjjInIKgpjjDERWUVhjDEmomrXo2iIROQMcLSWb+8AnI1iONFicdWMxVUzFlfNNMa4eqlqx1AvNMqKoi5EZEu4xTsSyeKqGYurZiyummlqcVnTkzHGmIisojDGGBORVRQ3eiPRAYRhcdWMxVUzFlfNNKm4rI/CGGNMRHZHYYwxJiKrKIwxxkTU5CsKEfkXEdkpIttFZLmIdAuz3zQR2S8iB0XkxTjEtUBE9rmxvSMiqWH2OyIiu9z4t9SjuOJ9vuaKyB4RKReRsMMDE3C+vMYV7/PVXkRWiMgB93e7MPvF5XxV9/nF8d/u6ztFZESsYqlhXBNE5KJ7fraLyDfjENPPReS0iOwO83r0z5WqNukfoE3Q478FXguxjw/4FOgLNAd2ALfFOK4pQDP38b8B/xZmvyNAhzier2rjStD5uhUYCKwF0iPsF+/zVW1cCTpf/w686D5+MZF/X14+PzAdWIKzwuadwKY4/Nt5iWsCkB2vvye3zHuAEcDuMK9H/Vw1+TsKVb0U9LQVoZd/HQ0cVNVDqloCvAnMjnFcy1W11H26Eegey/K88hhXIs7Xx6q6P5Zl1IbHuOJ+vtzj/8p9/CtgTozLi8TL558N/FodG4FUEelaD+KKO1VdB5yPsEvUz1WTrygAROQ7InIc+BwQ6tYxDTge9DzX3RYvz+FcIYSiwHIR2Soiz8cxJggfV6LPVySJPF/hJOJ8dVbVzwDc353C7BeP8+Xl8yfiHHktc6yI7BCRJSIyOMYxeRH1cxV2zezGRERWAl1CvPQNVX1XVb8BfENEXgK+Anyr6iFCvLfO44qri8vd5xtAKfC7MIcZp6onRKQTsEJE9rlXHImMK2Hny4OEnK/qDhFiW0zPVw0OE/XzFYKXzx+Tc1QNL2Vuw8mRdEVEpgMLgf4xjqs6UT9XTaKiUNVJHnf9PbCIGyuKXKBH0PPuwIlYxyUiTwMzgYnqNj6GOMYJ9/dpEXkH53a5Tv+RoxBXQs6Xx2PE/Xx5EPfzJSKnRKSrqn7mNkucDnOMqJ+vELx8/pico7rGFdx0raqLReTHItJBVROZMDDq56rJNz2JSHDtnwHsC7HbZqC/iPQRkebAY0BmjOOaBnwdyFDVgjD7tBKRmyoe43Q0hxwJEc+4SMD58iIR58ujRJyvTOBp9/HTwA13PnE8X14+fybwlDui507gYkXTWQxVG5eIdBERcR+PxvlOPRfjuKoT/XMVz976+vgDvI3zx78TyALS3O3dgMVB+00HPsEZBfGNOMR1EKedcbv781rVuHBGY+xwf/bUl7gSdL4exLmSKgZOAcvqyfmqNq4Ena+bgVXAAfd3+0Ser1CfH/gC8AX3sQD/476+iwgj2+Ic11fcc7MDZ3DHXXGI6Q/AZ0DA/dv6fKzPlaXwMMYYE1GTb3oyxhgTmVUUxhhjIrKKwhhjTERWURhjjInIKgpjjDERWUVRj4lImZuRcoeIbBORuzy8Z624WUpF5IqH/avdJ1HczJzZtXzvHBG5Lej5t0WkuomEi0Uk1f35Um3KjRdxsrp2qMH+z4jIj0JsbyEiK92/s3nRjTJkHMNFREVkapXtFX/rFT8vutvXisixirkK7raFHv+2RURWi0gb9/k3xMnkW5EtekzQvvPdbANeP8ebVeZgNWpNYmZ2A1aoqsMA3P9Y3wXuTWxIDcYcIBvYC6Cq1aZ/VtXpACLSG/gS8OPYhVdvDAf8FX9nwUTEp6plUS5vPvC++3tZ0PbCUDG48oFxwPvipLX3muBuOrBDVS+JyFicbAIjVLXYrWSbB+07DfjvGnyOnwD/BPx1Dd7TYNkdRcPRBrgAN15pi8iPROSZcG8UkQ4i8qGIzIiwj4iz1sRucdYfmOduT3LTEuwRkWz3qvuREO+/xb0yrbj76RfhmBPcK8U/i7O2xe+CZrdOc7e9DzwUdPyXReRrQc93u1/oiMhT7lXiDhH5jXvnlQEscK8c+4nIL0XkERF5QET+GHScCSKS5T6uuEp/FejnvneBe8zZQe/5nYhkVPn8XUVknfue3SJyt7v9JyKyxT1/rwTtf0RE/p/777JFREaIyDIR+VREvhAU2zpx1v3YKyKvicgN/2dF5AkR+cgt+3UR8bnbnxWRT0TkPZwv2qrv6wT8FhgWdJ6OiMg33fM/V0SGichGub7+SDv3vWtF5L/c+D4WkVEi8hdx1rf41xB/Yrj/xo8AzwBTRCQ51H4hvIkzKxqcv4m/eHzf57g+47wrcFZViwFU9ay66UncuIYB29y/s1+JszbNERF5SET+3f37XSoifvd464FJItI0LrbjMbvRfmo9A7MMZ/bzPuAiMNLdPoGgHPjAj4Bn3MdrcWdiAleAzsAmYHKYMq64vx8GVuDk4O8MHMP5z/UIsBjnoqILTmX1SIjjbAIedB8nAy0jHHOC+3m6u8f9EBjvvu84TlI1Af5Y8TmBl4GvBZW3G+gNDAb2466ZwPUZxr8MjrPiOc5d9DGglbv9J8AT7uMjQAf3uLuD3nsvsNB93BY4jLsmR9A+/8j1mbs+4KYq8fjcf5s7gsr6ovv4v3AyA9wEdAROB/07F+HMkPa55/KRKrHeipNRwO9u/zHwlHuej7nHaw5sAH4U4t9tApX/lo4A/xT0fCdwr/v428D3g/7O/s19/Hc4uYS6Ai1wZgvfHKKs8cAq9/HvgYdC/K1X/MwLKmeMG4cPWO7++1zx8P/naNC/Q2v3uJ+45+jeoP1G4KTlBufv7H3ADwwFCoAH3NfeAeYEvW8F7v/Jxv5jdxT1W6GqDlPVQTi3xr+uuPL2yI+TnuGfVHVFNfuOB/6gqmWqegp4Dxjlbv+Tqpar6klgTdU3ipMPKE1V3wFQ1SJ18kCFOybAR6qaq6rlOP+BewODgMOqekCd/4m/9fAZ7wf+rG4SNlWNlKcfddbSWArMcq8GZxAiz1GV97wH3OJegc8H3tbra3JU2Aw8KyIvA0NU9bK7/VER2Qbk4FRqtwW9pyJv0C6cxWUuq+oZoEiurxz4kTrrIZThpG4YX6XcicBIYLOIbHef98X5cl2rqmfUWUvhrUifsYq3AESkLZDqfn5w1q24J0z8e1T1M3Wu2A9ROSldhfk4dwe4v+cHvVbxt17xExxvGc6X9zwgRVWPePwc7Sv+HVT1Cs55eh44A7wl1+/Cp1E5Xf4SVQ24n8uH8/dS8Tl7B+13GiflSaPXNG6bGgFV/dBtFumIk947uJIPdwtfCmwFpuJ8SUcSrgLyUjHV5r3FQY/LuP63GC6nTLjPLBHeE85bwJdxFn/ZHPSlHslvcJoyHsNZh6MSVV0nIvfgVDy/EZEFOM0TXwNGqeoFEfkllf+tKs5BOZXPRznhz0eo9Nu/UtWXKm0UmRNiX6+uetzPS/wV8fhw7jAzxOk0FuBmEbnJ4/l/E+eK/mWPsQGUikiSezGCW9muBdaKyC6cZIi/xEl2+HDVz6Wq5SIScC9aQn2uZKCwBvE0WHZH0UCIyCCcq5tzOLfUt4kzYqUtzlVkKIrzpTZIql+HeR0wT0R8ItIR58rxI5wruYfF6avojNNUUbkQJ9VyrvvlVDGSpmWEY4azD+gjIv3c58FXnEdwmggQZw3gPu72VThX7Te7r7V3t1/GacoJZa17rL8m9JV2qPf+Evh79/PuqfoGEemF02T0U+B/3eO3wfnSveieuwfCxBPJaHGylybhXFG/X+X1VcAj7t1OxVrYvXCaAieIyM1uu/rcmhasqheBCxX9LcCTVH/BEc4knI7lHqraW1V74STk9Lqy3nqcwRx/qEGZ+3HurhCRgVJ5lNIw4Kj7/6eZqtYm4+sAnISAjZ7dUdRvKW5zAjhXYE+7V0XHxemQ3YmT/TMn3AFUtUxEHgOyROSSqoYbyfMOMBYnC6biNFeder9IEAAAAaFJREFUFJG3cSqi3Tjtu5tw+heqehJ4XUS+jZPVcm6EYw4KE2uROKuoLRKRszhfire7L7+Nkzp5O04zzyfue/aIyHeA90SkzD0Xz+Bcgf5URP4Wp2+i6jnJdvd7mipU9ZyIbBBn8folqvqCqp4SkY9xFqYJZQLwgogEcPqGnlLVwyKSg/Nlcginn6CmPsTpXB+CU/G+UyXWvSLyzzir0CXhnPsvq+pGtxnsQ5xMo9twLjRq6mngNbfiPwQ8W4tjgFPpv1Nl29vAF3Hu1oL/1gGWquq1ixv3qv4/aljmIpx/l4M4fRQ/dJv0St1tzwOTgZU1PC5uxV+osU91Xi9Y9lhTLRFprc4KXjfj3BGMc/srmgz3i3IXzvDKUBVlLMqcgNOBPzMe5TU24izI9GtVnRxhn58BP1NnbemaHPsfgEuq+r91DLNBsDsK40W2eyXWHPiXJlhJTAJ+DvxnvCoJU3fqrN73UxFpo0Er0VXZ569qefh8nDuhJsHuKIwxxkRkndnGGGMisorCGGNMRFZRGGOMicgqCmOMMRFZRWGMMSai/x8kYUmgYN596gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot the EC and apparent conductivity\n",
    "\n",
    "x = np.array([-3,1])\n",
    "y = slope * x + intercept\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize = (6,4))\n",
    "ax.plot(x, y, 'gray')\n",
    "\n",
    "ax.scatter(np.log10(df_AEM['conductivity'].values),\n",
    "           np.log10(df_AEM['EC'].astype(np.float).values))\n",
    "           #c = df_EC_cond['Depth'].values,\n",
    "           #colormap='viridis')\n",
    "print('R-squared = ', str(r_value**2))\n",
    "print('EC = ', str(slope), ' * sigma + ', str(intercept) )\n",
    "\n",
    "        \n",
    "ax.set_xlabel('Bulk log conductivity sampled from AEM  (S/m)')\n",
    "\n",
    "ax.set_ylabel('Log EC measured from pore fluids (S/m)')\n",
    "\n",
    "plt.savefig(r'C:\\Users\\PCUser\\Desktop\\EK_data\\Interp\\EK_saturated_EC_AEM_conductivity_scatter.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationship between log sigma and log EC is quite linear and we may now proceed to use it to estimate log EC from log sigma values at locations where EC is not measured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need to find the an estimate of the bulk conductivity for the top20m of the saturated thickness\n",
    "# of the aquifer. For this we import the water table.\n",
    "\n",
    "\n",
    "wt_infile = r\"C:\\Users\\PCUser\\Desktop\\EK_data\\Interp\\WaterTable\\KR_wt_depth_clipped.tif\"\n",
    "\n",
    "wt_src = rasterio.open(wt_infile)\n",
    "wt_arr = wt_src.read()[0]\n",
    "\n",
    "# Romve null values\n",
    "\n",
    "null = -3.4028235e+38\n",
    "\n",
    "wt_arr[np.isclose(wt_arr, null)] = np.nan\n",
    "\n",
    "plt.imshow(wt_arr)\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "# Now we want the base of our volume to be 20 deeper than the water table depth\n",
    "\n",
    "base_arr = wt_arr + 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a grid onto which we will be interpolating our AEM conductivity from the \n",
    "# water table\n",
    "\n",
    "nrow, ncol = wt_arr.shape[0], wt_arr.shape[1]\n",
    "\n",
    "xr, yr = np.abs(wt_src.transform.a), np.abs(wt_src.transform.e)\n",
    "\n",
    "# Get the lower left coordinates for x and y-offset inputs into grid\n",
    "xoff = wt_src.bounds.left\n",
    "yoff = wt_src.bounds.bottom\n",
    "\n",
    "# Create a structured grid\n",
    "sat_grid = grid_utils.StructuredGrid(delr = np.array(ncol * [yr]),\n",
    "                                    delc = np.array(nrow * [xr]),\n",
    "                                    top=wt_arr,#\n",
    "                                    botm=np.expand_dims(base_arr, axis=0),\n",
    "                                    proj4=wt_src.crs.to_proj4(),\n",
    "                                    xoff = xoff,\n",
    "                                    yoff = yoff,\n",
    "                                    nlay = 1,\n",
    "                                    lenuni=2, # units are metres\n",
    "                                    nrow= nrow,\n",
    "                                    ncol = ncol\n",
    "                                    )\n",
    "\n",
    "minx, maxx, miny, maxy = sat_grid.extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the depth indices to interpolate\n",
    "max_depth = np.nanmax(base_arr)\n",
    "\n",
    "max_ind = np.where(cond_dataset['layer_top_depth'][0] < max_depth)[0].max() + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding  temp_grid.tif\n",
      "Finished gridding  temp_grid.tif\n",
      "Gridding time:  132.05703353881836  seconds\n"
     ]
    }
   ],
   "source": [
    "# Here we grid the AEM from line data into layer grids using gdal. The data is saved \n",
    "# inot a temporary geotiff file. Larger gridding operations may not be possible \n",
    "\n",
    "# Define gdal algorithm as string - see https://gdal.org/programs/gdal_grid.html\n",
    "algorithm = 'invdist:power=2:radius1=250:radius2=250:max_points=10:'\n",
    "algorithm += 'min_points=2:nodata=-9999.99'\n",
    "\n",
    "grid_kwargs = {'conductivity': {'log_grid': True,\n",
    "                                'gdal_algorithm': algorithm}}\n",
    "\n",
    "start_time = time.time()\n",
    "# TODO Currently this is too resource intense, need to make it more effecient\n",
    "aem_grid = spatial_functions.grid_points_gdal(cond_point_utils,\n",
    "                 grid_resolution = xr,\n",
    "                 variables = 'conductivity',\n",
    "                 reprojected_grid_bounds = (minx, miny, maxx, maxy),\n",
    "                 grid_wkt = utm_wkt,\n",
    "                 point_step=1,\n",
    "                 grid_kwargs = grid_kwargs,\n",
    "                 depth_inds = np.arange(0,max_ind))\n",
    "\n",
    "print(\"Gridding time: \", time.time() - start_time, ' seconds')\n",
    "\n",
    "# Finall we remove the 2 cell buffer and flip up down\n",
    "\n",
    "aem_grid['conductivity'] = aem_grid['conductivity'][:,2:-2,2:-2][:,::-1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pcuser\\onedrive\\github\\hydrogeol_utils\\hydrogeol_utils\\spatial_functions.py:740: RuntimeWarning: invalid value encountered in greater\n",
      "  mask = np.greater(depth_top, layer_top_depth)\n",
      "c:\\users\\pcuser\\onedrive\\github\\hydrogeol_utils\\hydrogeol_utils\\spatial_functions.py:742: RuntimeWarning: invalid value encountered in less\n",
      "  mask = np.less(depth_bottom,layer_bottom_depth)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now lets calculate the weighted average of the AEM between the wt top and 20m below\n",
    "\n",
    "wt_ave_grid = spatial_functions.weighted_average(cond_dataset['layer_top_depth'][0],\n",
    "                                np.log10(aem_grid['conductivity']),\n",
    "                               wt_arr, base_arr)\n",
    "# Convert back to linear space\n",
    "wt_ave_grid = 10**(wt_ave_grid)\n",
    "\n",
    "# Plot the result\n",
    "\n",
    "\n",
    "plt.imshow(wt_ave_grid)\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O4AT8GW3961Y3/+ZB+zeL9jIWkUxALz2+C5wiIi8QEV/cLNGPYBYZ4sTl1vgTEelW1Qg3P18r2Xkn7oc6IiLLcBnv2jkgNWVqotgBvFBcetOXMn3u6hadpDPTiMhqXLa9adtgYVOoTnt8s6jrt0BeRJ4uLrXnO3H22Nnwc2AsHWQrpMdxpog8Zpb7z5USTqDuAxA3S/iZbdv3AMeJS92KurSg/wpcIyIr0n1Wi8j6BWovYxHJBPQSQ1UHgd/HaYSDuLzCv6/7Z6E4GC8C7k9f81+N02LBDRQVcBrVT3Gvue38HfCHqdfAx9J1r8AJ2UHc/G8Hy0X8t7iZT0ZxqSonp1pdtBSqszi+aetS1VFc2stP4h5KFR6c8nRKUhvxM4CzcAN7A2k93bPZf66o6p3AVbhBzz24pP4/aityI84FcLeItO6Zt+LSyv40vS/+G/e2sRDtZSwiWS6OjIckInIZzhPjhQcrm5FxpMg06IyMjIwlSiagMzIyMpYomYkjIyMjY4mSadAZGRkZS5QsMcos6O/v17Vr1x7pbmQsAJtv38rDHz3Zzfzo4I59u8EKos7vTQAU1IDkEnxjUYS46SGxTHg3qweIuh3EfX7kMjfH8V1j24kTD1VBRDmz99gp254td2/e7dpQt5x6ymTX+Zn5zX17QEAFMPCI4128z6233jqgqrP1TZ+S9U8t6eBQcvCCwK2/alyvqhvm095CkAnoWbB27VpuueWWI92NjIc4a7/wAUq/yhMXweaUZl+C19VEEyFfbJIPI8Yreey2EpLsjySxHpjjK+TzEdYa6vWAIRW2PP/t/MGPXsMDY70Mj5a499J3LEg/n/z0D2EiS1Lw+J9vTXaFn2afZ3yIqGg48zRBDVhPuOXTb5zYLiLzjvwcHEr4+fUzxTrtxzt285KIlMxMHBkZRwn3v/BtIODXndbsdzXpLLkI7c5CgxN7hoiqIbZoQSA+pomeUCPpi4jrAYGX4BmL51mMl3Dil96PbyzHdY4smHAG+MF33kJS8Kj2e5z9mqsPWv6Ciz+IxIoXKZLAzz/3pgOE80KhgJ3l31Ih06AzMo4iyqc2wVNMYCER6s2A7u4q1WbArXevdeaB0JKsjAkKEVEtQIyikWFoTxdhZxPPs6gK+Xydn9+7Fs9feIHU0pwvuPiDXPSU9/O9m9/+oDJPfvqHQEB8w83ffcuC92EyihLp7EwcS4VMg87IOJpoGrCCrfjYpoeIEltDpZzH5BNMLmH18YPkOxp4nkWGAkxgMYUYCSzNSkguiPE8S5IY/CDBDxZPaN10w9+gvvDUp33wgPVPfvqHEOuM5IdDOLfINOiMjIxFQyKD6Y7IF5qogudZrDV4foIIRA2fQhBxzqrt7Kt1sC+MiK3Tw+r1gGgkTz6MyBNRaYSYfIRvLOdd93ZuueT9i9LnG7/3Ni686AM8dd0HQcEG4uzjIpj48Ln5KkpylLkVZwI6I+Mo4v7XvYkzvuXyQKkKnigYS6mQsKxUpeBHWBUKXkTTeqzoKLMsV2VPrZOt1WWEvXXGqnkAckHsTB1hhOriJqdTI6gnSKxIouhhMmtMxi5cYsHDQmbiyMg4yohjj/JAiepIgfFygUo5T+AndIc1lufL9OfLFLwmj+3fypqOIUp+k0bs09VRI5eL6CnV6Cw06C7UCfyEYhDRjD3O/s7CDRROJgkNEismsZhYj4hwViBBZ7UsFTIBnZFxlHH3c99NuMensCXEv9dpw2f07cY3lv6wzAmFYRrWp5YEAAQmoRBEJKmpY6yWpxQ2OalzkFozoNIMAegp1Hnyf8/OLW4utAYDbWgwjQS/3GT9OZOz3R4eLDqrZamQCeiMjKOQzW9/I34dbAh2LGBPrZNj8mN0+zWKpsmjOrZzanE3y8Mya/KDlPwmD+/bRzGMqNVCosRjZW6M1d2j9ORrVOshA+USOwcXLkvqhRd9gAsv+gAAYt1g4KYfvwuJLaiy/ux3L1hbs0GBSHVWy1IhE9AZGUcpldWWJKdIR0zRb7IiHCdnIobjIlYNgSTUkoCciXjF6pt55vLbeMqx91AoNNmxr4eb95zMGd27+INjf0l/VwXPWKLhHKe/45p59etpT3wfFz31/RPRhD/4zlsOMGlc/4u/hUSRKGHDmYtnVpmMztK8kZk4MjIy5s19f/UmbEdCR1cNq8LORg+jsZu5K0GI1OPRpQc4Phiix1SxGG4fXo0RpdRR5/jOEX49eixdpsYxpTFCPyEc9Ig7Dl1APe1JV+CVG5C40PIbb3zblOWu/9V7Jz5vOOPBPtKLgrpuzWZZKmQCOiPjKCboaBJ4CQO1DiLrYVUwotxf72c4LuGJ8sjcLpaZOj8fPwlVoVrN0ZlvMNbM89Tlv2Uw6WD7eA+VRkhzeYJXFR79+rlr0U992geJOnxsPuB7P3gHN35vauHcYuP/XQGJJe4tcuGFHzjUUzBrXCTh7JalQiagMzKOYgqFJqGfMFLLs3l0OdvrPextdDIe5en3xzk+GGTcBvyicTwrwzEGqkUU2LW3h827VrCr2U3JNACIIg+vYhAL4ajypGd9eFZ9WH/Oe1h/znvwy03iosemn87etrzxNx9AA4Mk+qBgloVHSGa5LBUyAZ2RscisP+tdrDv/8kWp+45nXk5vvjaRja4ahywLKvTnygD0mRr7kk5uq5xA0WuwrFjD9xOwQjwe8Ae9t3BfYzl5P8ZaJ5hsCIWBmHAsmtWxqQhYi0SWH/37m+d+EBZsYFB/kX2xgUhlVstMiMipInJb2zImIn+1GH3OBHRGxizZ0PMy1ne/dE77XPSU9xN35UlKAU961oe56CkLH6031sjT31Hh9J49nNKxlwQ3QGhE6TYJgcTUkpA7y6uwKnQWG3ihS3e3L+5id6OLrdv7icZyLglTFfJ7q3iVaFpPiw1nvoP1Z78bSRSxFvU8brj1skPq//dufjvqC40en3NedfDkSoeK84Oevwatqner6lmqehZwLlAF/mMx+ryoAlpE3iAivxaR/xORL4tIXkTeKyK/Sp88N4jIqrTsOhG5VUTuSP9f2FbPuen6e0TkYyKSRopKTkS+mq7/mYisbdvnxSKyOV1e3Lb+xLTs5nTfcDHPQcbvDqoK1nJx7k9mvc+2dXkGzipy74sMDzzLcu8rhJOvXFghNFItMFAucW7H/UTqMRoV2FLpJ1KPWxrHULE5HtWxjV/sPo6RWh6rEI+FmELME/I7uX+8DxM6rToYM3gNMGM1zEgFqTXZcMqBQSUbHvlO1DjRoZ5w/S8vP2Th3OKmjW+l3iOowLkvWzwhbVVmtcyBi4B7VXXe6VCnYtGmvBKR1cAPgUeoak1ErgW+C3xDVcfSMq9Pt79aRM4G9qjqThE5E7heVVen5X4O/CXw07SOj6nqdSLyWuBR6f6XAs9R1T8WkWXALcB5uAfnrcC5qjqc9uMbqvoVEfln4HZV/aeZjuW8887TLB90BsA6/9KJz5vir8xY9vGXXsWuJymmr0EQJDzz5DsIJGGZX+Hft5/FTy6+csH69d0tZ3K8P8LmaDlbGisYT/I8vrSZEVtkfXE3A0nCN8Yfzafvejy+n1AeK7Dm2EEuWLGZG3adxkilQHVfia67fHLDSv8tQxAnSBSjRkAELeZAFeyBXhgLzVmvvZokL0QluOu9bwBARG5V1fPmU+8jHhXqF/5rdhMInLtm21ZgoG3VJ1T1E5PLicingF+o6sfn07fpWGwThw8URMQHisDOlnBOKZHO+6Cqv1TVnen6XwP5VEM+FuhS1Z+oe5p8Dnh2Wu5ZwGfTz18HLkq16/XAJlUdUtVhYBOwId12YVqWdN9WXRkZB2VT/BXECOJ5rO948YxlkxCW3W7o+EmR5N4OGtZnLM7zQGMZK4vjC9qvwaSD/6mezLZmH1UbEkjCTeOnc1tlDXsSy7gNuHV0Db0dVZpNn/6+cTrDBoNRid58jVo5h1cxmBiKe+MDKzcGRJBmDMYsqnAGuO0f30jUybzc/aZCERLMrBZgQFXPa1umEs4h8Ezgawva0TYWTUCr6g7gI8ADwC5gVFVvABCRK0RkG/AnwFRGrucCv1TVBrAa2N62bXu6jvT/trS9GBgF+trXT9qnDxhJy06u6wBE5JUicouI3LJv3765HHrG7zg3NL+M5HNIqcQlJ08dGv24F16FWAgqSjiuWF/5762nMtDo4JZ9blaPk69dOEFXsTnGbZ7RpECHV2dVOMza/ABGlFOCEnuTDqwK9cjnmJ5xTu4ZYGV+nJ/vXYNVQRseXlXovjciv7sK1kX7qe+Bl4oJq05IHwbu+ts30L2ZBQ8JX2ATxyU47XnPgnayjUUT0CLSi9NwTwRWASUReSGAqr5DVY8Hvgj8xaT9zgCuBF7VWjVF9XqQbXNd/+CVqp9oPT2XL5/XVGgZv4NcP/op6O5AgwcnhDz/BVcdcFeJBQS6izVyXswZy3bRG9aIyiFr/vVDC9KfvVEXp+Z2kTMReYkwWO6srnKadM2wNhjBiPKw3kGKQZNj86M0rEdnrsFoI480Db13KX49wTRS7w1VEAGr+78fRm795BuR7XtY519KJ73nzrc+RWiqN6tlljwf+PJ8+zUTi2nieBpwn6ruU9UI+AbwhEllvoTTlgEQkeNwo6F/qqr3pqu3A8e17XMcsLNt2/Hpvj7QDQy1r5+0zwDQk5adXFdGxtzwPPAP/DGf/4KrkETxG0q9xxAVhZGHQzDmfmr9YZmm9ekM6niFBIKFEXoGZUtzBdvqy7hx6DS+ve8sfjFwPA/UlmHEUrU+f7D8Vh7VtYP+fJnN5RU8ML6M3WOdDI6VKOz0yI1aguF6amdWJHFa9IQG7XsTg4OHi2RoBOzCTCjgAlXMrJaDISJFYB1Ori0ai3m2HwDOF5Fiavu9CPiNiDy8rcwzgbsARKQH+A7wNlX9UauAqu4CxkXk/LSePwW+lW7+NtAyBP4hcGNqp74euFhEelNN/mLcoKMC30/Lku7bqisjY05s/M0HKJ/czTmvdF4Hj37dNTRLgliorDDEHTD2cIi6lLigeKIEktDp19lS7sdGBm/Y58SPXTXvvuxudnHTwKncX+lj23gPD4z14hnL5tHl/Kz6ML4+eh7bm33kTMQTe+4hNDF7RjuJY4/GeA4VyA02IHWZQxX1jBPOIhD4aOCRdOfn3dfZsuGUt4AubFzfQgWqqGpVVftUdXRBOziJRfPiABCRvwX+GIiBXwIvx2nNp+IiKrcCr1bVHSLyTuBtwOa2Ki5W1b0ich7wGaAAXAe8TlVVRPLA54GzcZrzpaq6JW37pUAryP8KVf10uv4k4CvAsrRPL0xt3dOSeXFkzMS5L7uaJA+1FYJpQjgK6kF1lZIcV8fsyOM/rMxzH34bK4MxxpM8W+vL+O+7T0NV0LqHKcbc94L55aS4+Ka/IrIelWZIT77G/QPLaNYDOrpcIMuKzjKndu8lNDG7613ctmM1NjHE+/L03Wbou6OM1CIkdScEnLD2nBcHgA096isK2FD40dcPISjlIFyw4UrigqG2zKO0O+YH33Eufl2yjDEdmlckyymPLOg/fHvtrMpefNJd8/YaWQgWdUYVVX0PMNnK/9xpyr4PeN80224BzpxifR143jT7fAr41BTrtwCPnbHjGRlzQVxwR2GPEnUIQVWprXCyxN+aJxwTKoUSPBzOKdzHdWOPppYEnLxqH1sHe4kGQjScv6Z4wwUf5YxvvQcRpRl7RA0frfjUg5BoNEfgJaxcPsb9tT6G60V831IZLBCOGnrvrjlzhm8gSlDPQ6w98B1bFYzQ6DHEhYWN+nvak64gLvlYX/DqFr9hJoTzQmKXUBj3bMgiCTMy5klu1OJFLnubiSDqEAp7lc774ZifJXRss0hfgy2Vfu6oH0+3XyUQS3euRhjGaF+TQmedE784/yjDylgeaw2xNdjxAHIWP0jwOiNGx4pE6jEe5ajFASf0DuOPeRzzswRJLKYWQWxR36B5H5sLnOYsbrqquDNH9dgCzU4XUHL+8+dvmgG4+HGXY6oRXiMBhZuveys/+8KbDigzzvCt823HDRL6s1qWCpmAzvidpj2wZLEIR2OCqiWogF93c+7lhxNyI4pYJTeS0HFrgbuHlvPL8gk0bMBxhWHATaSE9u4AACAASURBVF8F0KiH6Oj8g1qDfMzKrnFyfkJxZQWahjh2P3MbGa6962wG66WJ8h0PCCayeMNVZ3cuBMQ9eeKOEFvwUd8Q9eSJevI0+kOaHQa/Cl4DrA+Pecn8ov6e/PTUi8U3fO/7b18UrbnFQg4SHi6WTk8yMhYB8TzWF1+0aPVfsOFKTKyoCEno/J6L+yymqRQGIlSEqMMgCQzt6OH2gdUUTZNev8J9I30kiUGbHsl4gBYTTnvP/JLlb37eu9gx1M3Ath6qowUwijGKMRaaBr2/xO6RLnYOdpNYw+gpFr+aoIUQDX1sYIg6fNQTos6AxvIiteUB1RUBUdFgEqXR48wEYiE/dGgeFme/5moe85KrUd9p5zf87PDMrpKozGpZKmQCOuN3GgkDpFRctPqtL05jHmhSGLJ07GiSG44p7KoQDNUxsRIVDVEHeJ0R56+8H4BIPTxjsfeX9ichjoS4OP9B+2Y9wB/1EKNIPiGJPcJcjKl5qAf1wQJBkNCbr1Lc6QQyQFIKsTkPmwpN9YQkJyThfoFlYgjK6lKSjltyw8055XJe8+krOf2d16AGkhw0Ow2bfvyueR/zbJhjJOGSYOn0JCNjEZBiEQlDLln1FwcvPEf+3x9+BPVkYo69wq46fjki98AwUq7h7RshLhiSPDR6laTi07Q+/f4Y5SSPiLNb+6M+Ehm8cW/qUKq5oqDH1+noqaKxIcxFdBdr+MdWsaGCKI29RX47uJzKaQ1qy33izhw2MJimdYN05YhGt0flGA/1BBMrSSCoga6tTTp2NPHqCXHJJ8kZnvyMgwfcnPntd5PrbFBbaYkLgvV4kK15sbFqZrUsFZaONTwjYxHYuOcf2bDs5WgUs774Iq6vfn5B6t2w/FV0dXeR9JYw9RgpV91gWuAj9abzG+4qUeszjF9QRVRY3l3hNyMrGWiUuH3HapqDeXwLJoFw2GBzYBpwytcvx/Msv3nOZYfUt/tf9DZe8NNX8Nvh5YzHQm08jzHqAgG7I2gYpLuJqkBsiEqAgXDPOFJr4Pse449cQa3fgIAapbzaw6+D2avkdleQyIV8az4gKYZUV+d54h98mB9+48Gh72s++0E6emr0Fuv88JkLEzl5KLh0o0tH+M6Go6u3GRmHwMahT6JxjCZ2QQYNLw6fjx0rY3fuxts5CM1owttBotj5EFs3lVPtkjFUBT9IqDUDdg13cdv21USN1KxwbIMk78waftXZdZPYY1XP2ExdOCj1xGdopANpGrTmUS3nWNZVpdBZJ+hqYusej1yxk0JvjcKAxfoGGa9CnKDFHLVlbmYVFZdS1K9CMK50bq0jjaY7RiNpQIvgVywmOtA889gXXcVp77kGL7SU93Tww3VHTjiDM3FE6s1qWSpkGnTGQwKN4gWJSmvPBa2JxY6OOX/hMATPuORCYUDSU6SyKkf9gTwaKloTGgUFBYkFzVuCZXXivQVyA4ZwHJqdzsbb01XFoDz1xjfx/QsPzZXt9m3HkYwHFPZ41I6NCfMxnbkGQ2NFCoUmfpDwm8FjUBWGTvdY+22XXU/zIep5BDUF4/qThEJhwNJzd9klS2oFt4nQ7C+R5AyiYI2zz1z05CvQwBD0BhR3Ks3uHFvfeHhNGVOhCskSMl/MhqOrtxkZh8jBcjfPhg09L8MU8kguhwQ+pqcbKZWQIJgQWsmyDuprexk5tYNan0EN5Hd55AYNuT0eEgs2tEgimN+W8MtOOEusqOdc1wDGmjm27etlzb8dmtYZj4R44x6NPouUYvJhxHgjR7K7SPm+bpqNgEo9pF4O6d6SZq6L3JuAWIsaUOMGQcMxpTAQQ6JItYEkFkkTKJlmgkmUcKhOYWeFdU94L6IgVgnKCfkRy5YlIJwdgp3lslTINOiMhwybkmtZ51/KxeHzuaF5CEnIwgDJ56Gz6OzNxiCqJMUQVIm6c9h06iZRpbRHibo8rOf8hgv7FLGGuCjkRgTUrferSlQSJIFGvyUq54nKIf5gQNdu4ezXXI31hNs//oZZd7W4soLnWcb3doAVRoZKYAVjIRwx1Ls8ajUPfyCg0ZUeS6OBlA1acP7YapzHRvf9dYK9bo5DRNxbQmrSSfIeXjXGNBOkGaOJYkMPNT43bXzr3M/xIqIcfRp0JqAzHlKIEZB5/EiTBOKEpLeEWCUJPOorcwBERVeviSE/nODVLMvuVJpdhqggRF1COAqFfYAqNnQReUkO4iKoD1iBLSW69grWh6gIcd5pdGe89Rp+feXshHRPqcaeoS7wFan6YJ1pxasL9WNi8l0NmnWf3HBI930RphlDEKK1GlIvkR9OyA+BV7eE9w9Aowm+D2HgkiiJEPUWAPDqMVKpO6HtG0zdzmlm78PJ0TZImAnojIcUNzS/zDr/UtZ5f8Sm5NpZ7XPJ6teB7zt/6sQitQaeMUgUI8U8rMxhIiWoWEyseHXr/Ih9IT9Qp3RfAw08mssK1Pt84oITEs2A1AVPMDFIDMf+xBLnhagA9eXCsrsT8oMRKMQlj7P+/Gr8Ktzy6TdO298TP3oVtrOPruVlmkFCXUK8AacVxx2WwooqcWzo7q6S1As0ejyK91ukVEDHExgeo/g/+yDwkc4ONAz2v/Q3I+zyHqLePGKV/M5x57WSJveXeoTmAy5+zGXc8L+XzeNKLTzKnOcbPOJkAjrjoUc6WDhrIa0KUYR2d4JnkGodGR2HYoGkK4dXc/X5DYsooM526zUskii2GEJsafSmwlnBJEpQTm2841DcbSf2UWNQgfwARAWD6fLd7CzjMUHFR6y62a8t2MBNEdXipGuuwvbEEBnGhotI2Ye8Jaik2ro11KshIkqUePgRBDVL0pnHS13niJrgeUix6MwZxjgh3YzAM3ijFSRKkCRBqnW3j7ff80FzATYwPO3/vY///tE7F+SSLQQKREsoz8ZsOLp6m5GxALRs0eBydcw0gLi+8yVIsTjhUiaVmtMi+3vRwEM958GgRrCB05olURfwUYuxgSEp+CR5g/VdcIaXuqOFZUthUDGRxYbGDbxZ8OqK11AQlzWuUvAwMZhej6BqiYoGiV1KU5PAeS+9GhWIOoVch6F02ijVRkhlTwlTN0jV4NWgujrBX1HDB1b0lDGijNODiRSv0kQSi/q+GyjM5yHwD3QdbCVOKuScq93wKORy6ZyFlo33fHjivK17wnuRRLn4cZcftjDugzO7XM9LiUxAZ2RMw/rulzrtsVrFdHXSWNWNaST4Y6nW6Duhaj0hLhnUOC01Pxy7yLuCjwrY0BCVPGwA+RHnIWEDF0oNLrmSX0lcHmYgznuIQhI4O3Q45kKrvUidMG0qBII01bm3eYL6aeh0t1If7uCElUPUtnYiCWCgusrSs3aE47pHacQ+9w8sQxVKBsKRCFsMMda6Phgz4ZUizQjixK1rzR4TxWy8+0o2rHg11237uynP3aYfv4t151++yFdobigsqSjB2ZAJ6IyHJJvir7DO+yNgBi06SZwGGfhoR5EdF+TJDUFpVx6vqagRwrF4IhS6484BNwPJ7gG3rxH0hGNdXas7sT2e05JFKO1qYH2DiS2mFmMaMbYQEJcCJ5xzLq90x84YNUJuuIEaodkTYnxNNXEXSOIlSr3PEJfAhopteuwe6aLvlEFGbu8n6ouRhqFSy7HLdLGyY5wkMSQ1n9yIJS75BFGChm7WFEnSN4W6m8dCrXVTYHmG63Z+fOL0bNz7zzOfZCOoyJKyRx9tGvTR9TjJyFhANiXXIp6HBD7rO158wLaLw+ejSTIRgai5kKjTMnp6zPBpLj+FX7NOQDYtpXvGYO8A9p6t2GrVLZUa+pstSCPGa1iCmqXZaZBE8SqRc0+rxU4LLgRIbCfMI0E5IRxL8JqWcKSJqUWYRuxyTsdKWHFlgqrzSRYLfgWCcSH/QEhybwfj1Tx2bQ1pGladvI9Hr95BYoU77ziB8NdFiAxJKHi1hLgzJO7JuzcG33Ddlqu4bsffc92Ov089V2JspTq38/vjd7kw+Hhhp606VFTlqMvFsXR6kpFxBBDfx+RySGH/XHvr/EtRqy760CokCaZSwzQEf8yj/1cxxZ018tvHCUeb5PZWkT2DaDNyQj2KU+Gezo69ay/hz+6i46a76fvhTkr3jiFRgj9ac5pzYFBxSZf80QbBeBOvGhOORUjkzA5qDOoZ4rzBNBWv5tKE+pUENUJQVsIxJRwVTAOsB0kiBEGChpbYup96Yg0d93ks/1XMqhsNUUkYOzGP9YU475GUQjTwWP+o/RnmNu79Z7TRQOuNOYfKm0aERAsz6et8cYOEWah3RsZRQyt50oYVr2bDspeTjB6YA0NjC2qJt27npPfswvR0w4plVE/ogmU5bCh03HS305bjaH8YdGv/JCEZH3frRZByBcQgYYCNY0SEljiQMIQ4xizvQ1f2YGoJpIEwthSQ5D38upuGShJn244LxplDKkq91+WkjjoVPbZOX3eFrrDB5p3HMXT7cmp7V5DkQDzwGpbC7hrdt1XdYKC1zrxRdw8VaUYHnqfyZydMQnNh4/9dAcC68y/HqzTZeMeUs9odJmTBAlXSSa4/iZuKT4GXqupPFqTyNjINOuOo4FCEw1zQRhNbqztB2lpa25JUAzQCNqFyUjeNXo9Gt0cSGmyt7spMNQGz2v3rVVGroBZtRmAV24zc0miQjI9j6w10eBR/1zBmcAyzZwjTjPEaCX4twau7dKDWF5LQZZvzGkpQtRSG0oE9C+zLMVIuclxphI41o9hQafS67T33JoSDNUy1icQJUq4iYxXMWBUZKyPVOtdteXAOkJZL4joz5TSgM+KN16HlxneEcIOEMqtlFvwdsFFVTwMeDfxmMfqcCeiMDOD60U+hzeaBKycJawlDpLMDr5YgiQuFjgqC+DO8iE4htFtCemKxSVtbFm02XV+shULeCdJmmt7TE6zvBEiSF5LADVCayHmDmMTZoXNDhvpggZ/uWENXvgHH1lGDk1IKZvM2NxDYaDq7cxyj42W0XscOj0x7OJuSa0Fk7g/MKEYaTRf0cwRZiIT9ItIFPBn4NwBVbarq9CdtHmQCOuOoYbG16Ck14DZkZT+VR6wkKXiUdjbIjVmKe2Nk9TEuiZLvg0nzVExZQTpVVCvcvBVy3l5enVato2NucE7VubnhEhNZTxCFWp9PnHN+yV5TSfJCnBdyQy4ABgV/zKPRCPCNxQ7k8GqC14DcSATHLHfCuWXeqNaw5Qq2UsNW5zYYOBs2/vZDEw+DDacemRwdrUjCWWrQ/SJyS9vyyraqTgL2AZ8WkV+KyCdFpDRlo/MkE9AZRxWLJaSnrbeV59kI9ZP6KOwo03HHbsJtg5TuHSE3WMeW8pgV/Zi+ZZhCHpPLYfL5/cJaBIyHyeXwj1/lypUKiNcmpI3nFgCbuFSm42W0VncmiFrT+U0LxAU3N6AoLlLRF9QIJsHNyJLK+7gjARWGqwW0mLhfu8LIyTkXfOL77gGQ5nfWKHZ29IPlKkm1/Tlfi1yIdpUgDOa23wIyh0ljB1T1vLblE23V+MA5wD+p6tlABfibxehvJqAzji4OouUeChOCZrLmm343hQKmp5vC3XvgngewewfQ0XFktIw3XHHd8gxSLGCW92FWLsf092FKRbwVy/H6+/FOXouceDy2u+Q8RoIQCUO8jpIznQQ+Jp9Dci7x0oQHSBy7sOsoxhuuYpoWG4izM+P8pQ8Y97JMpAr1xz26u1z/8BSJ9+/TWNuHFvMuQX+97uzvgPgHF56b7NfSB4+Zk1fHdfdd7cwpIqx7wntnvd9CoQqRNbNaDsJ2YLuq/iz9/nWcwF5wMgGdcVSwKbl2QjgfyiDVrGlpvcZzPtJpjgkJAuzQyH5XujhGqzUYHmX85A7KZy7HdpcgTrA9HWipgJywCj22H3vCCmxXweXkMMYJRiNO8HZ3YXq68fr7kM6OiXzTpqMDSYW364CAEUyi+BWLX7PO97nm5jU0aZQhLeFcB/WUkdESCpjhwKUPrbgw8tETcy6QpJBDo9hp/nPRbMU4U80c2XjH+4j6i4yvLfLo189vBvO54kwc8/eDVtXdwDYROTVddRFw52L0WXQRNJLfNc477zy95ZZbjnQ3MjhQOG+yX5tfXVO8oosfYAp5VJ3/syaJ+29bnhh2vwkg/SznPoLq6gJeXclvHyfpzLkIwXIdqdbRfC6NLHRJh/CdFmnqTYgTJE5tzamvM+3h1i0TiWeIuwvYvEdc8FAj+LUEG5g0k5xMmDrqPYaoQyivtajnXPK8qhCOCSaC/JC63B0dgldXeu+uEdy9AzsyOqG5zzbTX/s5nO0+AOe86mpyI5bCvogbb3zbQcuLyK2qet6sG5iCFY/o0z/8wiWzKvtP535xxvZE5Cycm10IbAH+TFWH59O/qcg06Iyjiv2v14fgSdDGxL7tnhqqE5ot4GzPIvuFc4uW9wVuwC/qCrGe0OzyiHsL2JyLNFTPQ4t5pBkhSWqT8A0aeOClA4axyy89MYDYjFz7qi73hecS6NuC027VOPOGidMZTSLrQs6Hm0iSRhmWLfkhS+e9htyAAetydZi4NYWVy4IH0Lk9Itw2CFFzvzvhYeAX//JGOreUCXfNb+7FubCQbnaqeltqm36Uqj57MYQzZAI64yhkLpraQZlkd9Yodi5uLc05ntl31xSLVI4JqPcaTKREXT5J3qPRm6OxqoO4t4gW89hSwZk5Qo8k5zRgAA0DtLPotncW0MBHA38ikxwAsUU9g4bGzf9nW/7O6X8Fb6yBiRWxionBryvhuE7Yp72G0OhVKquU8glQPiGdlmosQot555cNc7bxT1yLOdqjN5zyFrzBcZcV77Bx9IV6Z5GEGQ851vmXOjNFaxJZkbZgkjSIxIgT0iliptCkxaBJgg0Ev6Z4DetyQQdCEjrh6tUSmitKWM/ZjyVybdqcj6l6aN5zGeRsOi9gMefyPOcCJEnAAsaVj4q+C1IJXFi4pgJcjSuT21elsbyI17AkeYMXKZ0PwPBpkOQUGzh7tQ0Em7N0bDOYKEHGKq6u1vk4BCE9W+G84cx37DfhiJ1IyHS4WErzDc6GTEBnHJVsSq5lnXnenGZGmZYpwrNl0muuWnVCus0KIJ6HhCFxHjqG3aAd6nI6xwVDEgi5EaeNeakpwoapQFbQQoBECeoJouJyVqi6kGtr3TRVUYztzIOBYDxKTSNuLkTTcHk4TNMitSbSMPiFgKg7cAmZGs7kkR/y0qROLh0qQG7EEJZdRj4t5tE9eyfOw3xt+1Ox4cx3uHkPWw9DEdQzbNzx9wve1nQ4L46lk2djNiwdXT4j41BYiEHu1kBcy6Sg1nlqWD3ALjuhQYuZyILHscvJjyjqiXN5S0OwWxq0y/UcYRqJE8oG4qJHkvfcrCRpIiENPGwph4b+xHpp2aFFsJ77qaoIoi4HdZL3J7R/LeWxnXmSoj9hp/aaLrm/V0/tzw0Ix5XiXktuzJIbivD3jbnE+20zohyKl8ym+CugdvpxgThxs4Wn53njbz7gglcOI3MMVFkSZBp0xlHLJvs1p0Wb54HIQTXp/f7O7eaNNo8MSF+/vUnr7IS7HRK4AJMgwPQvo7Gig+LuJqaRYHMezW4fG7iovtKeBK8aob7Bhs77AsOEHbm5LJ10tZHgjdadumRBU79nPJNmsRNMYicGFlFnd1ZfSHwPE1mafQWXka7oubDv9GHi1yxBBcLxBIkVvxphyk03XVW5RrJ7LxodaAc+VA16U3It67w/Yp153kQdG/pe6eZz7Cxx3V0fPKR6F5LMxJGRcTiZLqx6EgdqhfuF84Nsyy1bbMvFLUWTZL+QBmczjmL88Qam2sTmQzR0s6qgil9TgnKCzfnOFS7NG219QQsAQtTl4VctZixGotgNDpq0D420j8bgjTeQJMT6ZkK82Jw3EUVoPUnNJBBUErDqBgxVMU2DzRnCfTXMeNVNazUyBkZcSHl8YNa6eZO+0Vyc+xNMd6ez5ccx17VNh3WkaHlxHE1kJo6Mo5p2rXlGt7sZBLkYccJ3QpvWA/+neTMmMtEldsLLwxscdwKx3kSaLoDEa7r9hk4PiTp9kpybj1BTDdivpgOFvpvHMOrOkfQWnQue56G+QUt5UEWSBA086svzJAUfm/NIik6vktimgthp09YTgrEm4UgDr+pySfu1BK9mMdUGjIxhd+3Bliskw6PYcnnBIzM32a/tfwPxnHlm4+AnZt7pMHK0eXEsak9E5A0i8msR+T8R+bKI5EXkvSLyKxG5TURuEJFVadk+Efm+iJRF5OOT6jlXRO4QkXtE5GMi7tcmIjkR+Wq6/mcisrZtnxeLyOZ0eXHb+hPTspvTfcPFPAcZi88BQnoK++m0odxTMV3K0MmrEosdHsEODqM537nTeTIxe4goJHlodnnYnGDDtp+agGm6WVBa5oik4Ltk+Tkn1FTE+U83IlQEr25RXzDNBBO7AUcNnOAX6wb7RMHUY8xodeJ4JUrwGqmdu1xJZ4hJDkyDuoBsWPZyvO4uTFcHYszBp8U6jKgKsZpZLUuFReuJiKwGXg+cp6pnAh5wKfDh1Ln7LOC/gNaUv3XgXcCbp6jun4BXAg9Plw3p+pcBw6p6MnANcGXa9jLgPcDjgMcC7xGR3nSfK4FrVPXhwHBaR8bvCpOE8AFa9WTNuA1NEpf280Eb2iIIJ2+Knc+0GRzD1GJnIzYuoVFQcV4dzVIqbOM0HWikmIbd78McO28MiV10nxpBfeelQWKh0cQbraTh3M4fWiKLaSbpgCGgzp3PL0fO9BL4zp3PKrbgu/kE6w2kVEyDYKYXzvP14NA0IEcKBbRUOOLpRSdztA0SLvajwgcKIuIDRWCnqraHDpVwpiFUtaKqP8QJ6glE5FigS1V/oi4u/XPAs9PNzwI+m37+OnBRql2vBzap6lAa4bMJ2JBuuzAtS7pvq66Mo5hZmzraaXlrTJdsf6KcTpg/DsjlDGAVrdbwRiv4o42JXNF+XZEkTZ6fZpiTZH8bziUPRNUFmTTc3ITqGbyxOma0guYCtKfTBbBIaz/F1GK8eoxfbro6Ep3Ibqehjy3lsAUfEyWpT3WaXySKD/Dtnor55DnZsPxVLvIyjrnugY/CaBmNl8Z0V7CwkYSHi0UbJFTVHSLyEeABoAbcoKo3AIjIFcCfAqPAUw9S1Wpc9qgW29N1rW3b0vZiERkF+trXT9qnDxhR1XiKug4gzf/6SoATTjjhYIebsQRoeRG0mFFQtwYBZ/uan3pyOPe7/XqNeKCNBlKrQ2cBSawLFMkZCnt1IvWnGiHJGfx6Qhx4qHHmDTWC5n03eBd4qJ8KUmtd0qWcR9QRoL4h8XHadpSgxkN9N0u4+oKpO39qG3iQauI28PDH6tSO6yAM/FS7N8D0QvNQNOiLCy908zYWi2wc+bf9G4ywcdc/zLm+xWQpCd/ZsJgmjl6chnsisAooicgLAVT1Hap6PPBF4C8OVtUU6/Qg2+a6/sErVT/RygW7fPnyg3QxY6ngZvwwXBw+f382uqkS48Oh22Db8nBI4CO5nDN17BnCHyjjj0fO7NBUcmPJhDkDEaIOf2L2k7hoSPKGuNNlufPKDYLhOtJ04ddJR9iW1yP1oe4ISLpyxF054qK//44W8GoRphnjj9bxx+qob6gfUwKF+ppeZFmvcxFcINvz+s6XsL7jxe48+/6BwhmWnHA+Gv2gF9PE8TTgPlXdp6oR8A3gCZPKfAl47kHq2Q4c1/b9OGBn27bjAVIzSjf8f/bePMyytCrz/a1v7zPEkENlkZRFQQk2iG1jiVIPcsV7+8qQGdWt4HXqoqmigMICQUSwZXACGeyL4MXrgFgXiioBKbHEBvtSmVmNtF69gBagDIJCg0wFNWVVZkbEGfbe3+o/1vftvc/JExEnIiMiIzL3+zzniRN7Picz1l77Xe96F8fry8f2uQfYH7YdP1aDcwQT27LPFDratFI62UWLzixH+33k1BKiJnETbx2DsZAnXstiHyL41JzmYvu39DOkN7RJ4kHCJ5kpNWJ6oU4sMIOpPWK7d8uZpK/bwrdTcA7JLZuPxkraaRlHXG/KKb+0CctWweE9zxz5PQ7f3enwyFSvnYKtDNBfAR4nIrOB+30i8FkReURtm6cAn1vtIKr6DeCUiDwuHOcZwPvC6vcDUaHx48BfBJ76KHBIRC4Imfwh4GhY96GwLWHfeKwG5whiW/bIWCmoyeY2+AdYzzxDs4v2B0YdRPOj4ZD0zhOkS+ZQlwxMk5zPOvPQKBS8UnRsVFUUDEgvFPiKAkLXoGS17FstmAOkPSsqSu5LeZ3kVQHUz6Tk86bLjrx3+4Q1o0i3s+ITxbQt84fnS1EURxdv4ujiTatsvXOgCrl3U712CraSg/6oiNwCfBzIgU8A1wN/FIyuPfBl4HlxHxH5F2Av0BaRHwEOqeo/Aj8N3AjMALeGF9jQxneIyBewzPnKcO7jIvIa4O/Cdq9W1ePh/cuAm0XkteGaRp/LGpxbqBf06gGpbOteZ6Zdbu8xYRKW8cag5834KFnOmL3Dhyw3Mc1zKhRd6/xL+p7O/SHQFh5SB1mkHwpot6x9vJtStK2b0M4DvuVwwwJRB4WpP3zbWZBWRVO7lnw2KSkQnzpcluP27rFC3nCIH2b2OeIghCl8TQ7PX2M3Eud2TWCuYyfRF9NgSzsJVfWVmNytjhUpDVV96ArLbwceNWF5H5hYdlbVG4AbJiz/Iia9a3AOYqKrWr21+7R1owNbp0ad8oiWpN4j7TbaH5DefZLiwj2lragrZwdWh0hODoPvRg5pgrZS86vwoZGl2wp0SZQ6YTRHYqZLbujLZ2B1VTehmTLZiqLjjIO+qMNMMU/6dZPbqdqNQfNIeTjz01gFh+evgVAoPbYLg3PkoHcTdk4u36DBGSIG5/FRTOOdguX7cQpko8gydDg072gNNMUww53skZzqW5AtjOpI+tbuLYXilgdm0F8UNrS1k6DOzHDNbgAAIABJREFUmVa58KZxLoLHc+Zxhe3vcl/SGzL0EGgMdTZRJWqmXW666+EeR/tEbp7SMx3Ue3OVCzSQpC1ct8Ph2atX/IiH568pfbKP9d555t/ZWYKqTPXaKWi8OBqcE3hy8pOTg604C0RekQRwKRRFsA4NGfCG1Ry1LDooRnSYmfRNKt+MZLaNdNKys88tm22ozrQt6y7MDc/1c1TEOhP3dtGWZb9JsBVFwQ2LSo+UeXwnwbedNbkk1joOYUZhLA4K9A62aZ8s0HQ/abeDW+rhTp5Ce327IWRZ2WQScXjfs60IKoLMdHclpTGOnVQAnAZNgG6w61GNr/KVZwYQ9b6iUgXqgHUH55U006p2Pm80hySJGRHdfwJ3YD8A7mQP9s2aF7RX45tzj4SOPj9j/s30B0jiyPbPWXAGC8SFNahoYhlyMihKHbePKhEnEEZhuYGndXKIpo7lB87gW5Cr0FoKQXy2BQ5ktovcdRxdXkYLP6JSiSoNHWZIu8WRu/9guu9pB0N193HQDcXRYFdjEucsTkZpjrHgXC5bD1ZTgAR+W7M8dCZ6yzwHNi1Eshx3qo8b5OWxpChgMCyPF7noYt9MWeQDC8xSeFxW4DLji6PpUjk+KwRuFcuc437pvUtc8KkTdE4qPrEhAj4VirkW2k7J95vdqZTt3zU/5xisi+KcCM4GofBuqtdOQZNBN9j9WCHYjgTpmFXHobB+9VmDK2KFLNrGn7iST6YojIkIY7FEBNc3pzpU0STB9Yd4EdzyEE0S/AP2WicglJQGYtcsuccVBT4Nk7/LwC2IDwqRuYS85Uj6oUNxxnzAlr7FoQ7m7vRoIuRtR9GZoX2/SQS1KKy42esB9kQiScKxXaJtXg82i18OirNT2GNafqYTx1fCzrlVNGiwCYhBuT79BBjRKkO9ULiJj7zqbQrLMLNGkSQJhcNAX/QGyDA3OiMyGP0BFB4/37YJKfFyhx5XWDEwjofSxAqAmtrvvpNQdGyQrB3MtNKawvBA11rCRege93SPK0XHVaqSYLzE3nmk2zF+ORRNJUk4Nnz35n0vOwRb4MXxg6r66K0KztBk0A12MQ61n2ZvYlffpOAc1kEIyt6jW2CzWXl7mD5as9xojmGODkHaVhAkL6BlnLgmDp03mkETZx2GUDndDaNpP6VXB6qlPK/Y24FYJEyMi06XLUvufnMJ8oLiwCztU1U3oknxtLQo1U4bObDfWrZj8J6d3fzvZydAN63LfdvQZNANdiVGgjPxrY64zWmeVb+H+YJqlaKVD1xved5gdq1FYdRBMD0CjD7oD5D+EOkb9+z3z1HMtcn3dMytLmiYY1egTUTJkcwms6iT4HyXoa3EvKEzXzXBtITlgymDfY5itmV+0t6kdknPmmNaiznt+zNrkPFKvr+L3zdrDSxpaCG/9EEc+r5Xb+iz73Sso9X7ASJye+113dihFDgmIh+bsG7T0GTQDXYdnpxeeZrWecTLua640AJVMYkdQQrn81p3YRgEGHdtt8tmE4J+WofDUR+OiPF0LGTR4gRJU3QwMPkdQJrazaHXQ/IW0mmhSRspPEWnZbrlYYEmjnwuJV3MkEEBDvI9bVxuU761mxoPLUK+p0U+46wr8b4Bw31t+hemJEMln01piZDefYrklEnlinm7EWikSdS47Wxfh2N/OzIjg+//iTfyAz/6Bv76vb+wgX+hnQkNRcIpcc8a1MXjVfUOEXkgcJuIfE5V/+rMr3IUTQbdYFehrtqw7HjCc+v471F6F72fy8Uy6tkhDtfpIGmKm59DRMr3rtMZ4WhNGZJMzLKTB1xoQd6rHSMqNcLxbDCtM8rCK0kvx7dtsKxvO5Ll3KaszLXI9nUp2s7mEXql6KaBDnH4oOaIk1zS5YLu8YJ0ydNazNBOy3TWmcny3LBAisrESRNhcGGbwQWn52nJUHFD5fE/9saN/lPtSKhO91r7OHpH+HkX8GdsUXdyk0E32JWY2q2uPvg1NKtIu2XFO7B5gqEtWtptoyaGo1OutfC4B1yI9PvW2AHW3l0UaEE5g891O8jcHNnDvoXk018MzTCm6BBNg3rEQ8f4aBXBFQWS+VJa5+IklBCQ7cLihSjatqDuE4coiIfWYk7RTcn2pmSzLgwLsJsAziGDMBjWOWjZjQABl3s6x715dox/bV4pug433GWk7RrYDBWHiMwBTlVPhfeHgC3hhKbKoIMr3L8RkW8T2Yze2AYN1o9D7aedTm2shVA8lCSxnyIWtCSYD0XZnQsTU7K8SqFcLC46dHERDh7AXbAft3cPyYELLLOencXNzZLMz+EOXkj/e77VJph4X+PDzT5UhyFQ5gUEbTPO2agsLCi6YRGuScIkFNtFxboI23culsXDomM+0UU3gURIlwt8KmSz1llIXlgreRifJcPchskWimQeyTzJ8pD2fYPTvra/+vOXki57OvcNedLjX7u+73yHwrLjTWn1vgj4axH5B+Bvgf9XVY9sxTWvmEGLyD7gBcDTgDZwN9AFLhKRjwBvVtUPbcVFNWgwjjrvfJpaYyXU+GDA2rwxqkGd6ZHjOul0IE1hedkCNpQFPtLUKI28QGe7tn2WI85Zw0mnTfagA2jbUbQdM5/9Jj5eZ8xn4jFD8JcsR4Y5vhtaqQsPag0rOptWvhpqkjtNpOS4XW6TxcUnaAKSe4qWY/milg2iLdRGaA0zuxkkzj6LWgejywpkUNjcQ9Vy0O04/vIDL+VJP/BacMIT/+2v88G//MX1/JPtSGxGJ2EwXPvuM7+atbEaxXELNv/vf1XV++srROQxwNUi8m2q2th1NthSRN659HmOGHeoO82EPmTFqoE3HrUbjfPzJEmQ2RnLZgeDytvZ147vxFzn+gML2GmCzs9C4vAzNpKKQkmGnuzSB5AuL1vGrB6ZmzPJXVHYvnGqiQfXzyxIg2XUuVEfRdvhgtJCFFyvMM56eYhbHjI40EEdZLOObNYxc1eGeOicKGidyksDJaJqJT4NFAXJfcvVZ0wSjn7yNSt+9xqy+ThUYLdjt8nsVgzQqvrkVdZ9DPjYllxRgwYrQf3IPMASEwJzGcjFlUW68jBhSGyphxZBhxl6yUFkcQnptFFV3MyMDT2NxxoEbjrw14LJ5ZL+ENfLLEiLzSWU2VnjD4sC6bQDX+2RmRSd6cAwQ1RRb9NWNEjs6Fr2HKd1S+7RlivN+/P9s8Fu1FnTioDLzQ+6c6IgXS5o3XnSsmewwJwmdlOIQTp0DOpMZ02S84N/9Usc+r5Xk9x7ioVHvowj//T6Nf6Rdi4Uwe+gNu5pMFWRUEQuAx5a315V37tF19SgwSjqmbKfIHeDkXbvepateYYWrvKbkIpXJnLEIpDZTEF/6bfYyCjvSe64F5ntop02JA7NCyQvbKhru4Wf7ZY0AYVHspxW7vGdFsVF+1F3Acl9S5bB5wXSFbRt2bZoah4a7TRor0FbCXk3weWeIjVKxLcT6yjMPMW8yfLSk32SpQxSRzc1RYcE6sKd7CEnF604GT+vL5BsBp2bQQZDKLwF7byA1ukFwnEc++ivcsUlL0RPnORQ+2m7ustwlyXQawdoEbkBuAz4DJVgVLEZgw0abA9CoFlxXQ0lN10LyJY1exCjSTTwrqbsSIx6wDLWfH9K+yvHq8MPM7TdChlogXbbRpGo4tsprj8MFIiHfECy3EdnOvj5Ln5+xnjq3tBavVVH6IWiawFaUyHvWlFQvFYOdYBbtM+dLA9xi8GAqRhYsXOmbU0rSwM08Ny6Zw45tWSfMbebCRjvHTlpbaXQSusS8FVx69d/x4yUdhtHUIdunhfHdmGaDPpxqvqdW34lDRqshpUmosR1Zat1+BmDdtyv1oqtvmoBl1i8C0FaxQJt9i37aX31nirbVLUgHQNUy/TMrj80bhiQ3sACoAjSH+IAbadI7kemplB4aKX4GQvORcey4KJrvHP/QOCkCyVd9kG/XEBG8I/2RrekCbRS0zkXHrfct/N32vZ5YhFUpPwMtEb/5LWzdgYdcVvxHg7NXMXCxS/YcRO7p8Yuu79ME6A/LCLfGWYDNmiw/Vgra1M9/aeu0PlX+mVA9M1w+/dBK0XbLaQ/IO0PjMpIK/ldzJglN/N86Q2q8/mWBeDIdTspm1HKzgcRNE0sSAdaQ7ziO4JvVT4c6gABnwgut3mD2kpgWCDReCmeNy+QpZ5xyeEaCdSI5qYyIRZHazcwTauC6bG/e9X0/w6Am59Dl3vr2mcnYbdl0NMw5jdhQfqfROSTIvIpEfnkVl9YgwbrQt1DY+p9Av0x0zH5nCqyuGzBtwguciOqEa0yWLBA1esjS71yzBUilsXmhR0H7Pdyfy07+yQrzLUu8+aXMTS/jHTZ0zlRkAy0nJYClo1HuqK0NVW1RpSoOBGBXt+8R/KiCujhmoDy+n17/X1qR+7+A8gyFvZfu+59zzYU8F6meu0UTPMvdANwNfAppmasGjTYZqyXGw3B2e2dD4HYRlTpYGiqCxHIckQDfZGpye5iVjzMIM+NwwZI2pa9DjMLjIC0UqQ3tOw60BrltYqEhpGCJLXJKJoDTkhP5WgiJP3YIdgin2/Rvn9AshyCfmqju+KNRDxQeOuCjMXP2LkIVuDsmD+0tlum6tggpGua8Sse9DPcesfvrr3DToECuyyDniZAf0VV37/lV9KgwXagRnNIu21BeLlvlEavj8amliJ0+cUstN2yzDPSBP2+vU+snVsGw2CG1DcD/FYK/aSagKcK3bapOGZa+JbDDXKSxYEpM7IC7bSQ4FSH2OQTiYNiC0//YBe3r033m0vmK73YswCc5XatMYv2hV2/OHRoum3pdkrePE5LkWyFousaOHL8rbsvOAfsthrnNAH6cyLyR8CfA2VPaCOza7CjMWmG4NgyzXJTPszNIFlu0jQXqJJQZKMobAp23KmVWkAUB5npoqXbteCc58EBr11REWDvQ4u51mRtxUwL323ZdO7gZhcza1CSxaC7Do0w6XLBcG9KMdcmiXROaOfWxaVAr+QVddPvW4OML9Dh0BQkiT0p+G5n5FrWi90YnIGzUiQMjnePBx4E9IBPA7errlb5NkwToGewwHyotqyR2TXYuZjERbsYjIL0LvxtyJ75KnD7AkiqykxRoJk1fKhXC3b9QRXIIXC9lfGSFh7ppFVzTJqaciMMkyV1FN3UCoTBtIg4YGUYMtogAXRDj7ZT0pN9sgOzuEGBaMLgQIdkNsXlXdITA5ITPtAahf1siQ2aFbFsOSpVwueRNEUShxTnm1faVD4bm3c2kR8EXg4cAD4B3IXZZfwI8K9E5BbgN1X15ErHWPNfSFWftTmX26DBWURNEw1UWebiErJnrmzhllbIdr1HB0PjesMAALyvRliplNppwDJXr9YAE/XI3sNMJ3hhBCVHtAiV4FwH4CinpIj3wdjICo6R7khPDCy4t4S0UIpugi8UdV1IHe7kqTKwk+XWHBPkhubSZxRIaRblFXqnmySd89jeDPrfAT+lql8ZXyEiKfBDwJOBP13pACtWCkTkl0XkwCrrnyAiP7S+623QYJOwmmqjLkWrbzf2RCnOMkzpB/64KEaKZ9LtWNYszgqCw2Gl1IiURvipRW2U1jCzLNsrMhgig8wKharIoCDpZeavkRW4QU56b890zmAmTkGdgffI8qDy1fCe1pJNUEGg6LqQgVsno/YHaJ7je317PxzaDaV+zOhHHVzurrj05zbrX2TnQ0G9TPXalNOp/sKk4BzW5ar6X1R1xeAMq2fQnwL+XET6wMep3OweATwa+G/Ar2/oyhs0OBOMmR6tWvmZxEPHVZMM/71WxbYksSx6YMHutEKbV/B5GHEVOxY9FEYjkOcWCF0RuvYUt9izDDZroe0Ud2LZ+O92yzTSSe2zRe8M78ELijWztBYLfCK0Bh43KEjuvJ9iedmuJ9At0erUJsJkxo07VxYITeFh2y9c9HyO3Pnm9f0b7Fpsv4pDRPYDz+B0u4yfXWvf1cyS3ge8T0QegRHcFwMngXcC16nq7lWrNzg/MZZxixPc3r2m0BCxp9/IJ8cioS/wg4FRA51OGdQ0C9uN3wCirG1pqZyeonmOhEKjttLqBkBN8hZoFYk9NGlSM+yvrjvpe5Jegc4muMzTuq83ej3jiC3u3mEOTLXjOakom/MFZ+ejfgD4CBuQKk/DQX8e+PzGrqtBg03EehpRVsqso9OdONzcDDIfJlinJomLRcEyiNYDX+CZo0WpxmYRGA2QWi2TILnTMJtQ8gKds0nebmlQmiyZLC50GzoL2Iq1mft2agNkBxmzX7rf5Hi+TbI4gLuO40+dqnxKnM1dlMSVtIsUHlJXGSgV3jTRORy56y0sXPwCFi56PqjnyF1vmf473o04OwG6q6ov2ciO51sZt8FuRtQwbwQjPHRwtvOVv4YMM8tke5Uig8SZ14UqZNlIsNZaK/dpI7JC5iuuPpgWU3TMdtFOWnHjzjJnBmZdKnlSmTEFVYg7tWyHiU5687MkYPx0nuOjtaiEyTFR4hcLheXAAoFBuMn0+xw5/tbqs66t+Nr9OHuNKu8QkZ8C/iujUuXjK+9i2F3mqA0aTPM4fpo/dJ2zrv7L63Bobdpl0a8wvrYoRlq3JU1Nw6xaDZ0N22hm3YQxKMehAqXlqYbgF4z6tduimOtUE1Lywm4S4x+h8OWNQ3oDOLGILvXQwQAZDHHfvBdOLobOwaIKznEYQI1rllj4DIFcQ8t5bNc+cueb7XtJEhYufsHa3+8uxmYNjV0nhsAbgA9jPvofA26fZscmg26w+zBtJh0D78hfXGX6L84ZpeE7xjkPhpbNpilgE8All5GgXioiouSu5pqnvqJPCHMQgdBpOIB2GwrFZQX5ng7tnikppPDQ7VSfDSxTzjK7PpdY52KWQatVNslo1GRH974kscJmoGLiNZQ8sxPIwo1ElaMnbig/1pE732w0x7mOs+Oz8RLg4ap6z3p3nMYP+rcnLD6BdcK8b70nbNBgU7BWkI7NKONctCoIYeJ2gROpSeySsqgXt9VhaOxot0IjSlGth8BDF6PXEoNz8MKo0wzSH4ADl6VkB+dM37w8AMK2Pjjm9Qfo0pLdCCJVEc+5vFxeZ30u4/jkGPsIahQNVC3esVNyErIhh/c8k6Onblz5u93FkE3MjkUkwTLhr6vqapLjzwDLGznHNBRHF5PVxWLhZVhnzLUi8lsbOWmDButB6Wk8jpWeRSOtMAX8qUW4N4zcFAvaxjdnozxx7ASMwbI+FGDsnBIUGVF7XOqns8zGYeUe18+QoaeYbeH3dC04Z7ll8f2BBWHvSz21dS86y6oHA3x/UA0h0CorLuV/EZGKKUdgSdkFuXDwuSPfxZE73zw61fxcg67jNR1eBHx2iu0K4O9F5A9E5Lfja5oTTENxPBx4gqrmACLy+8AxrAPmU6vtKCIvBp6DfeRPAc8Cfgl4KiY3uQt4pqreEbZ/BXBt+EA/q6pHw/LHADdibecfAF6kqioiHWyw7WOAe4H/oKr/Eva5BvjlcCmvVdWbwvKHATdjN5mPA1er6miVp8GOgszMwHBogW6iMqPG90aoWnt3zKLr69WbXbTXWgAbmvtc5JuDd4V0OoEaML2zBt65Hhjr54zbGPWRVZdYeFhetq4+wO2ZNye9NDHDprxAl5bRxaUyQ498dvmzXowcO78EXbNqUVIsQKkyKXnoJKl9L46Fg881C9GAczVzNsimFQlF5MHAvwdeh1EYq+G/hNe6MU0GfQkwV/t9DniQqhbUKpLjEJFLgJ8FLlfVRwEJcCXwBlW9TFUfjVU1fzVs/51h/b8BFoA3h0cIgN8HrsOaZB4R1oMF8/tU9eHAm4DXh2MdAF4JfB/wWOCVInJB2Of1wJtU9RHAfeEYDXYwRjLW9UBrKgpxZUZsE75rAavVQma6tmm3g4SJ3yNIkrIoODE4l+ccy6zj4tgpGG40/vj9pfe0ZDna61dt2TBSdIw/y1csAI59Vi2KskEFrxWV4TW0mrvqe4yfP8tZOPCcab7NcwPTZ9APEJHba6/rxo70W8BLWUXXLCLXi8j/AbxXVW8af01zudME6N/A0vO3i8iNmOnHG0VkDusmXA0pMBP6zmeBO8aMQeaoHiieCtysqgNV/RLwBeCxInIxsFdVP6zWS/uHmNlI3Cd+0FuAJ4r9ZR0GblPV46p6H3AbsBDWPSFsS9g3HqvBDoUOh1VGeNpKHf0ZEQKsJMmoqoJRpYWbncXNz1nmHDLqsmXb+zANOylbwGWl65h4bWNBuh50NbRmn1pEl/swGNirtn7F19hnHNknYuw6NVIwiXHzR+58s6k3An0zTnecs/BTvuAeVb289ro+HiJYXNylqh9b42w3AN8NfEBEPigiLxOR717P5U7TqPI2EfkAlokK8IuRkgB+YZX9vi4ibwS+glnsHVPVYwAi8jqs9fEE8INhl0uwbpuIr4VlWXg/vjzu89VwvlxETgAX1peP7XMhcH+ka8aONYJwx7wO4NJLL13pYzbYLsQMcKVRVhG1oFUW6mJmGQqHEjllCAEr7N9KS5mdqpbZbKQDVMMxRCkTp0mZdN0HpD5JPATNUulRFFCA5sFbOmbGq2XodcTPWh/jVUerZdSGOCSuL0a3O3L8rRacN/KEstuweTroxwNPEZF/h9Xo9orIO1X1qpHTqX4Ei2mvEpELMUfQnxeRyzB69Yiqvme1E02rg3aYF8dx4OEi8r+ttUOgFJ4KPAzzQZ0TkavChf+Sqj4EeBfwM3GXCYfRVZZvZJ/VjjW6UPX6ePc8ePDgpE0abBN0PUWrmpg18sVlMQ0sWHU6SLtdZc7xHLUgGQt9VqC0ieJSD4SRInDJyE3htGupoybXkyToqmsKC60rSKaREZY3gtpxQ/AvaZroxgfVTWksmB+5+w+mLqrudohO91oNqvoKVX2wqj4Uo2X/Yjw4T9jnXlV9t6o+I9C7v4fRtatizQAtIq8H/gYr7v1CeP2ntfYDngR8SVXvVtUM84/+/rFt/gj4sfD+a8BDauseDNwRlj94wvKRfQKNsg+7iax0rHuA/WHb8WM12IFY2H9tFRjXG0TiY/04L+wcMjtTddjVOOfYvi3ttk1FiY0eI40f4ZW2KspjjYAqbjTL1ZjJxlbx0DxSyuZWuylFdcm4o1/k2p2YXtpJGIlV455j9j6GsqvwXMfmqjhWhYj8sIh8a+33XxWRfxCR9wPHVfV1ax1jmgz6R4BHquq/V9UfDq+nTLHfV4DHichs4H6fCHw2mC9FPAX4XHj/fuBKEekEpcUjgL9V1W8Ap0TkceE4zwDeV9vnmvD+x7E7mQJHgUMickHI5A8BR8O6D4VtCfs2Wu4dioULr7NW69jEsRFMCnRZZnxv8GjGBZ+KoHmOmabMzZUdhBEiYmqPxFXKiJXOU66rCnilymP8ZlPnkKd9YlihIFkWOGNQrhcVz5e27m2Cqv73VTTQr8OYh8hbXwU8G4tbU5meTCOz+yLQYhXFxiSo6kfDxICPAzlWXLwe+CMReSRG4n0ZeF7Y/jMi8h7gH8P2LwhKEYCfppLZ3RpeAG/D+ty/gGXOV4ZjHReR1wB/F7Z7da3v/WXAzSLy2nBNb1vrs3z+k1/likteiObFeWTLeHZwxaU/Z9KzwiPzcxaE2i1ckuBPnBxpEpkasWCYhpbqJDEjo1Y7SOqyysWurikeZlXhriiMkw7eHZWiQ0ujo/r+JUakf2P8eewMhFFTprUQj+kqyqJUptT59aB51mFhHYX9wWiwPg+xmY0qU0BVNTao/CjwtlBY/JiITNW2OU2AXsZUHB9k1OhjTS9TVX0lJner48cmbRu2fx121xlffjvwqAnL+8BPrHCsG7Aq6vjyL2IFz/VhHc0PDTaGK771xVao67ZNJtYf2JSQokDm55Be3ygAv4EgEwt1ibNgFqmJidlsoB36/croPjasRH11mPq97uJapFK8Gv1QDMps3OL3lJ9t0hACJ7iZrt18Yvt3UZR8PMHg6ejiVAqvcw/Kdrd6i4jMYzH0iUA9u+tOc4BpAvT7w+u8xSMuewi33v67LFx4HYf3PRsR4cj9aybeDdaBKx7yIuuc67Tws22bOB04VMlyNLHingN8rzc9DRARXexcjZbwoW078MPRDEkitxsyY+nUKJZg0k8I9Oo2kNHH69HT+WBxYW7eap/PTSj2BV7c1rvQ4u3LIqnJCP3o5z8fsb0Z9G8Bf4/56H82JJqIyPcA35jmANPI7M7T222Fz//9l40PDVBVDu95ZtV6q4okyfmbmZwhrnjIi0ouWPpDnEjwSPZGPaQpLPeQudnKi3mdVEdlXGSeGyLJCEdrk1Cip7Irs2ZppUZreLUp3l6NQhhW/+5aG0K7+kWMaZO9Yjo7X0oALWAHymS1rsn6U8S4zWj4jFr4ssU7FjzPd2wnxaGqN4jIUeCBwD/UVn0T66peEysGaBF5j6r+pIh8ign3HVW9bJ3Xe84guoAd3vPMkQLSuWwysxW44tt+PqgMJGiVQ1tybHXu923DMMlEFxctEwwZbfnYvhpqlII4j0haFfeiJ7TasFelKrCV2ahX6PWr8zlXzg8ETLI3HFb0cgys9QA7ybAJTPRZ96eO11TfZhJWuhmk6ej3GbJnF+cqts6Kk9vOwjYGaBF5aLCe+PrIJZjwgSB6uERVvzZhd2D1DPpF4ed5Pxj2EY/+VvgSoRjjOLzv2fa4WBQc672Tw/PXlNuW773n6PI7zs4F7wJc8ZAXWabqBHJvwTkoK/x4Iauo7D2jV4akaeiem4KzjTRC/acT8I6qZ4lRlzcno1l1yPDxHg3nl+jjUWuCKTEpOE8IuiOZfZz2shYmFCKl3UJaLbQIRc0sR/PMjh8ldxsddnAuYXspjjeIiMOUYh+jmuv6cKxB74lYjW79ATpGeVX98iZe8K7FkXut03Nh/7VVsBCxgByCcT1Q4yyQS5qW+55NPOkHXkt6XygksK+mAAAgAElEQVQo50Xl/+B1RJlyxSUvtMkfMx2OfO7/ZOG7fhlNHe7O48FRTTjyjd/b8HVccckL7Y2qmc+3WqUJkA+ZaoQEtYWb6ZZPKpHioNUqFRYrot5VGANfLBCKQJGXo6HiuvjvqoU3lQVUw1vrTR9FERQSWTX+KnyuEUzBlVfa58qrekWKYyW0al2PzhmlAaNywAn65/MJ0zShbCZU9SeCx9DTMXndxVjB8LOY6dvrgtBhRchaXVoi8qOYwdADsYcysXPr3jP+BLsEl19+ud5++8oDEA7PXzMSDOo0x8KF152WvZytuW+RUrj1y28y2eBwaAGyNv5o4dtfapwrwDDjyD//Bocv+5VqsgeYDC4vuPXLb5r+3A/6GQuyvf6I/7L2ByEw6erZY71bLhga+cGgMi8K25TURK17TsQaN2LXYKQ2AJu2DeXv2rNZyFVHXzDfFzGZXT2z9t4+wzAb1TavJ7COZ7WTfK4ntbLXl7mE5OCF9t3mOb5v/05uzuYt2lOH7Vd3rttNEJGPqerlZ3KM7oMfog9+4XSjAf/Hy19yxufbDExrlvQUVd2nqntVdc/5FJynwdHFm1bkno/ce/3oH4UqCxc9n4UHPm97Lq6O3Gw1Fy56Prd+/XeMf13ugTgWDjyHKx70M1acA7PAzHIWvuPlyGCIDDPLdLMMTRzaaVXZ8BpYuPgFZSNImWl6H+xDK68KXUmyVqMPymywlVphrJVaR1+7Xf3eCu3ZIThLtxMaUMK+YdoIrZrPdJoinXbZbn3atdQKbKXKgxDIz0R+OYmbXm3ukrjT1rm2PU3ocFgF526nGnwbLVddUyTcjFbv7cQ0AfpOVZ3GlPq8x9FTN64cqO96i2XOtexoux3Ebv3Kb5XBaeGi53Pk3uurCSESHvXzGqebJJYta9DsBgWDLPWQ/hD1flWryoULr7MniDxH+/1R5UWUhY09dtfn+wGnc7utlqkVRIx3bbftZwjORLN8sCy31QqjoKpZfaXpUYQL7dyTONq6Xjo2gdTkbPGaN5Q9lx96QoY87bHCDUiLQLs5wc3Nlt8JEMZind/0RoltbPXeDKym4vjR8PZ2EfljzHC63qjy3i2+tnMS0mqFxgGgyFnYf+32aqqdBC1vMAVKkopySFNu/er/PbL5ocf+GtJOkUFmj/eBowVKedr4Z1g48JwqsHpF8/rjuKt+FoVxwLXAPUJzRC/j2GTSDu5sLvLIDopA4Y3NH9TCuucoCsiklMppnIgyRjupqk08qfPJ8byxQAjltG9pt8sCoq6nC7COScF4LQ57rEnHzQRPkWGvemIIDnaaD6yYquHfu5ZBX3HJC1Hvz6iesOuww7LjabCaiuOHa++XMT+LCMXMjxqsA1c8/Bfsjym34tStd17Pwv5rTRWSZfYHFvSqdVpk4dtfCoAUHk0jxzqadR35p9dPdQ1HvvF7ZeYe+XGJFG63wxUP+hluveN3q0NnBZJ7dH6GI595EwsXPR9FoRiW6gGy6kazcOA5VcEqMx9nQcpCnCSuohikmhIyEpilKmxpLfGTJKlm+4lAEo4RNwjz+4h8dj0I53kYH+VLC9KSh05M5uaXemWWLKIl9wxUrnZ1X+k8N2OlJEHz+CVOWdzbiKJi/NgusesqvP07FFUALqeyqJaKo3hDWbjo+fbZzsemle2V2R0G9qjqLWPLn475Sd+21jFWU3E8Kxzs8ar6N2MnePzGLvn8xRUPe0mY3mFfeSywrZQ9L1z0fDMJSi0r0uglHAKdOkG8oiGQLXzHy23HNEHbKSrCsY+9auKxTY7l0WG/yoaTxApkgSOPyo6jn3g1C//6FchgyBUPeZFNf37g80Z9hUPhLNId0mnbU4IvSnOeESVBQlAbhGDonGWh8TPWMlaJQ1OTBNqtUXtQryUXXVIXpxkDBU45dNNpnldKkHiePK8ojHjzCPuro2oNL9UfvgzOI8VEXWeQtg+yYfmb69o4Lh0Mqk5BajGoTveEz7Nw4XV2k/O7MJ3cBMgZlAs2gF9jNNGN+CDwZ9ggkVUxTav37wDfO8WyBivgikt/LgSOzIpzU2AlU6Yr/tV/soz5n39j4vrDl/0KKoJkhd0UBsOQOVaGPtWUahsmqsMhx3rvtMAbHpGveNhLYGjXe+Sz/9nWqbLwwOdx5K63cHjPM0spV5mJhuCmvV6gIRJri45Bzyu4kMFKahl4p2NPFDMEzrht8jknJrvzIUtXtSw4CVljniNpAhq4c7XjuvBIL0kI+pGHznJ8r1+Z5Ysvdc2EBpQISdOqAy/oroFq+GoY5joyemrcJ1pqy9ccjzV9MI83qMi96zAr5YG2osaZ13eNNExRcHQHyD7PE8yq6t3jC1X1m2Ei1ZpYjYP+XzD/5oMiUtem7MVyoAZToHRnUz2N390Q/OopwNFPvmbi8oULrzNKAEYojMOzV5d/zEfueos9/oIpLmoFw3KdExYufsHItJGYucYwI53Qrhy4Ys0V8SHrLdRiV6djj9ghk1VV4+cBbaWmqsjyoJKwx3Tf61sABlMteDVfZ1+UAUghBN6aEiIz/lziNI0wNFbCtqp2TZIkqM8t6IWAXwbFyKdnK6g26kqTkfFaDqgF6TNsFol2qKWcsGz9Dk8atUy6/n+laZoK2N6Hhq6IpFrvhgJEpIU5c66J1UioNjCPBfE9tddJKj/lBqvg8Pw16OKiaYY3IzhD6L5bP3d45N7rKyVJDUeX34F65cnplbbdnW8OUjPT9daVJmVWX5rXu4ov9r56xM5z0zcPBrWMs5LXGUdc+xxhaGt9oog4h9u7x24EweRHkgRd7tnk6xiQs6yiKoJbWznJeqYLrVZ5A5E0rbTQIYiW5kjhfTkYQByu0xkxStJIgwSspNseUaGsJsHbSLCO31t4IqkbJNmNxM59dPEmji6/o3w1oCwSbqPM7r3A/1PPlsP7tzBlDW81DvovReSvge9S1V870ys933B437MBLECdPLVpx9XWNKzUxnCo83SODd5lhcQLryuzxsOzV5d/5Ee+8XssHHgOmuUcPXWj8c7RQCgWpGLAC4qJkWaoEFwsOzcVB2mKLlmDCIU3DjvQGG5+Dr+4ZMeJj/ExsEcemVB8jAXI+Tnj2btt3OIy3lsjDDFj1yqwSQj+pzW8tFtWL6gH23pr9zjqXhriTt/mTNuso31o+Kxa+JHvtjHqmhLbm0H/MvBa4MsiEjuyL8U86H9lmgOsmooFw/wDZ3KF5yssYFgQ2FQZXQxEm4jb8putwaPdKm8sR+693vwchkNrW5+9utxe+wM0zzk8ezVHjr+1CsAxk40mRjGTDZmrJGFWXrdr308o2OlgGHw4Qvu0Bs46+m50zChf5ubCPMEW0u2WUjtptZA9e8xEqdMxXfCeWQuwoYAYNdLaHxDnDZYZf11WFzsH68G5rn+OHPNqmfHI5O24/VgTymrNKJMgzhpS4jUUBUfufxtHT9xQmnc1mALbqINW1VxVX46N33tmeF2qqi8PYwDXxDTp2CfCDK0/AZZqJ29kditg4eBzjUMVz5HN/uPZImlUWQyr8ZZloQwqv4ra+1ICF03tYzaqaplpVD+02qaUiMoNETNHipSBFlU2HNUZgHZaSB6HtUatdsf2jdc2Y1y4znSQNEHTBHXOVCbRMnR+rtyepKhMj4oqQFezBQNVEK1AATQE6JD9i0rpmSFOVs6o680h6/XWqCHyzlp43Hwb7fWbjHkDELZXxSEiL1XV31DVnoh8h6r+SW3dr6vqL651jGn+2g8A9wJPwCQjP0zjcLc6shzyfEtMkjR1aLr5QfroqRvLQBtNn44N3mXnDIHsUOfp4SJ8SX8cmrmKo6dutI7EGKR9bVp1kpw+ASU2iwRds7Tb1QDX2NXnQmefiE1YgYomGGaV0X6aVNu1Uvs9cuKqpigZ62Asg3P4HHUuWULHYZTjlXI+rxWHHVznTuOgV8qKJ7RnT426w164/sbSdoPYfg76ytr7V4ytW5jmANMY9j9rPVd0vuPQzFWIyNYVZs5AN7sW4h/+4X3PLptYjg3exaGZq8qs9lDn6WVmeGzwLg7PX8Ph+Wsq7bKIUQ4ilekSmIJEpexYRCS6bllGnA0DvWCdetpKbfxVywpfmiTmEzLMkE4b2bfH9MizXbSVmDokjqBSj852cMdPQbttNI3PSz10LDDGAKtjxbay4SVqp+uqCUBa6ShnrX7yv0s9i15vBi2RvgmyunADO686/7YCmxB8RaQL/BXQwWLoLWG832mbrvB+0u8TsWYqJiIPFpE/E5G7ROROEflTEXnwNAc/33Bo5iqA0aLYZsOD5Fv7nHb0xA2Vbhk41ntntTJmjSOdf1JRFC4U2DqdUd5WQot2p2MZbq0pxdzm2kEVItUgVe8t+AZomlg23e2gLbNE1bSWncbjxuPkuQX/6CGSBoOlWmAujx0UHOWTQPBntpWWietgUA2LDfuXx6i3mp/Wrr3+f6/SW6SO83zg66ZgczjoAfAEVf1u4NHAgog8boWzTXo/6feJmOZZ+e3YTMIHAZcAfx6WNTgLkMj3bjXqBkBYthyLfPXgfHTxptJ/WLody267XfvZatmydqvy0YDKeKnTRsPIsLo9KHmODEILeF5rKPE2nzBmq36uW9IbOKoMNi9wp3pWfAx0iA6HFoSjoiOgNDoKwTnSGaXDXq3YqXE0FrXC4krBtwzWGzBRqnlm2Cir9vb8m58H2AyKQw2L4ddWeE3a67tF5KSInAIuC+/j7981zfVOE6APqurbQ0UyV9UbgYPTHPy8Q/gDH8k4NxvDrCp4bSHKAQV1t7pYKPPGIccnhqMnbgjrkqpRQhXtttFO2/YJXLGeOIkuLlrAXO6XmW60BwVQ701tMcjQoLjQThuSBL9vDp2fxc910Vbg40PnpHH/QUGy3Audf0N7RU458tHqa6/VFRU2TNYHc6hiVC9tG6y8/1pqjXFapNZ6LyKW9cfvYJ1zGBtMwPQZ9ANE5Pba67r6YUQkEZG/B+4CblPVj552KtWkZtGchvfx99b49pMwjYrjHhG5Cnh3+P1pWNGwwRji3LuthES98DbgyPG3srD/2krrHDJZqcn8Ds1chdu7NwTnYGaUWXOKDMKNJPLI3uMHA+ORW/1Sx+s6HbQY4Pbvq6gCETRNENWS5vCzHXCgnZDdJg63HNQcvYHtGzLsKjPXakKKjmbPdpAJdETsChwbslqffLIpGOOlywJkkARKYpSQ9m3IwbHhu1c5WIM1oetScdyzmmF/kCA/WkT2A38mIo9S1U9vwlWOYJoM+tnAT2KTaL+BdRE+e7Mv5JyAjvkzbMkpfDlcdVsQ6YyoP26lIwXQ6OshaSVDi9dJMBRCFb3vfhsOEKwvdTjEDzN0MKBYXML3+ibra7fQvXP26rbwMy20lVihriiMgx/mSFaQLA1wi8u45b7x0yWFYr4jkdYond3qAXaFzHbE23mljsAt8FaWVtu+y6jdjl7T/X41eqvBmWOTddCqej/w35lSlbFeTKPi+ArwlK04+bmEJyc/CcBtxXu29Dxlq/I24cjxt5am+/F3sBbxhYueb11tMx1z2QsUQ6mZdtij+XLPAnB9hqAq5cDXIMPz992HLC/j9u9D52ft0X6YW1AOhTk37FnwDc0jfn4WHMhyoET6fcjzEPCHpSRuhDNe1bzIfDosCBelf8fpnYFjgwTWo9Koja2SNA3NN6EZJ7Ssa78/8hluy2+e7tgNVsVmSOhE5CCQqer9IjIDPAkbC7jpWM0s6VdX2U9VdbIrT4OtxXZmzwEl9+k9h+evKZsk4hQSyUPzR7sFtcGvmodGlJryYVWIq2iUvlEWsUlFQ4auSTiPCNpK8O0EiX7ISz0rCg6H1fm0FnDXQ02EbcshrnHZZmXPqkjLZhzWrVS130d7/XIqN9AE583E5tRaLwZuEpEES0Peo6r/dVOOPIbV/rctTXgBXAu8bCsuZrdjq7NnYO2i01YgtkWH88ZGluiKp0u9qr27W5kLVROw/YiOeDWY50ZhNqmLS+himETuHNptoXNdtJ1SzLXx3dQCduCeNS9KiWNlK+rWF5zHpXL1YmL990n7TfcBq7dxMosz7Tje4xeXqlmNDTYX09Iba6s4Pqmq36Oql6nqo1T11Vt1yauZJf1mfC8ie4AXAc8CbgZ+c6X9zkfUmze2GmUH3Dai3rkWPTkOzVzFsd47zbUO0KVlo19axker94gEc/06xbAKpNuplByFDxNmWqZ3boUMWgRtVW5uLj5RpNaBWP5tiSsLtisOo10L4xah9WaU9cjn4j7xRhGP0wrSw07HngqGBMvTeF5tsudNhLCpXYLbglUJTRE5ALwEeDpwE/C9qnrfdlzYboJ0O1bA2g6Mt01vM05rwhlvd/Y13XL03AiKBB2/9nHetiig2y2DmKSheSXLjUZpt6AV5hQqgfpwyKAwxUgwYlJAyMoxVOaXUacq1vkd1q9xvb7O0YVO/ci/ncTBA4S6QmamUZrlpTfItE8dDabHbgvQK6Z9IvIG4O+AU5jl6Kua4DwZbm7WjOO3A7GR4izhNI23iDVWiJkkxTFLkTsWEQs8RVHLJOX097EbMa8aUaIpkvSHVoQM3tIyzC0gF4pkBW6QGdURzJkktphHeiNeagx6sRHkTFvmp56EMqEwGecbBt24v/sedGmppFAkTUsvlAabiE1WcWw1Vnsu/3mse/CXgTvqXTAicnJ7Lm/n44pv+3l7VO12t+eEabrpdqPrRQzSh2evLmVzEDLBQG9ExE48cZW+2TZ2Iy9JEpOTqS+DdVkgHAxhYEFa+ln18h63OEDuO2k66DRkpZFqia3fQckRhwuMBOmtRL39u7YsWq/iFQYDiuP3oYXH9wflNPIttQs4n7HLAvRqHPTZjQK7AFdc8kJ7ozoyRmorIfUi3FnEsd47rVhYG/aK61hziXOVB0YY0KowEpzLIl5E9IEuPCLejJCSaBvq0WwIS0tVx2K3E8yRAqUSvDkA5NSySe2GQ8rp3y440bnQzDKkmsS9UawkrRvhnItymZufH2nyKWWHsTsx7sOEJ5UGZ44p2rh3GrZXVHsOYeHgc0trzO0KzoBV+HcKohlSgPZ65Zy/Ug4YZhJG43zb0FOOtXRSOeFhKg6K4A9db2kP1qRAlRmDtYDPhcKic/ZMONOB4bAalxWvKfpTRze7JKnpozf4lzsepKWWscePKmLjs9o2vDZOmvH9mi48Tktx0nQMbiWaAH2ewKtZWG7zg4YuLq290XYiBtPw6A5BN+21nCRuU7JrN5aYOZeZt5nkl11/Yl7L5fvgF22TyKuAq+0W2kltJmsrtIWLQBucC6O0wkQYabeM1vBGIdBSoxNgssJkmsaT8glgQiYeC4MxYHc7IIIfDOyzOleZ/Y/YkjYPrluJ7TTs3wxs6f8GEXmxiHxGRD4tIu8Wka6IvEFEPicinww2pvvDtm0RebuIfEpE/kFE/vfacR4Tln9BRH5bwpRPEemIyB+H5R8VkYfW9rlGRD4fXtfUlj8sbPv5sG97vZ9r4cLgm6L+tCGsW44wF28noHxcj3RFqxZAg6F/XC/RU7mWXcahp2VRL7jNlY0t3ptL3uzMCDVgJw+ZdgjOpA4NU8tJHbpnzo4Tm2yif3PNHjQqKSQNwXsl46LVMKF5JaovNLjiuXbL7FTDIFqKwsaGlddSZc8NthbbbNh/xtiyAC0ilwA/C1yuqo/CnmmvBG4DHqWqlwH/TDVp4KcAVPW7gCcDvylS/s//feA64BHhFfverwXuU9WHA28itFsGeeArge8DHgu8UkQuCPu8HniTqj4CuC8cY30IGtjY9rydkG5nx/DQQOXrnAbKYhJlEGcAJsnoXL2a50VZFIsjveJx0jRwtL5a5oL0ziukrmwpx2NmShI8oWOhMcssS65bj9ad4erTUUpVSc1ofxwj62ufIxQ87Ukgs2y9ldq8RCf2RNFu2blrWvb6zatRbmwhNqlRZTux1c9TKTAj1rEwC9yhqsdUNf51fASI5v/fCXwQQFXvAu4HLheRi4G9qvphtb/iPwR+JOzzVEyfDXAL8MSQXR/GLACPB2ngbZiptmCju24J+9xUO9ZUOLznmaPBYpshe/Yg+/aelXNPRGlApKOmPmO+0dJul7P1YrEucsSa59X3WVOAxH10kg45zh9MrOU7sgxSKBLHVDkpZyRqnuOXl9H+AD/MoCiCamLMBClkueLEMmupRm+VnyuoTqL5f/29XWMlq3Pzc5X+OUwqH5f+wSiv3mALscsC9JZx0Kr6dRF5I/AVoAccU9VjY5s9G/jj8P4fgKeKyM3YFNzHhJ8e+Fptn69hgwMIP78azpeLyAngwvrysX0uBO6v3SDqx5oe3p+9f8NWakZBOwVjk1VK9YY4yqgZ26/bgU2KbeG14mDkgWPhzlq+cxgMQ6ANGXqa2lSVJEFn2mhi3YW0HG5gGmopFBlmMDtrRbm6F0gYphzFFZr7inppSXWNIcib0VL1+eI1TpvpLlz0/LIQqoOhdV5q1UATpX84x7FmEOyW4pzrJDwTBErhqcDDsGz4T0TkKlV9Z1j/S0AOxP/pNwD/Grgd+DLw/4f1k6JR/JpXWrfe5ZOu/zqMVuHSSy+tls90ofBbMhB2GqgTjvzzb5yVc49Ds9wKcD4U56KiI2aMwSNa+wNTcwyHRldobQZgnP8XJ3kX5t6mISrqcIjbt9cyz26wIU2dZc0hS3e5eU9LodUx0wTp9W19p4OPswr96b4cNoA2QYKyRESQTue0bTYySfvInW+euPzJ6ZVBG25mTse2aoZlgxHIWWzy2gi2kuJ4EvAlVb1bVTPgvcD3gxXwsMngTw+0BWFay4tV9dGq+lRgP/B5LMutz0B8MHBHeP81LMsm0Cj7gOP15WP73APsl2gSMXqsEajq9ap6uapefvBgbYBMkpy14AzsmOBch6rWgnMItiEj1kBh+F6/Cs6Rk47ccn3sVM2POc7806XlYCCk1cQUVaRnQVmyAnevNavI/adMOy1i1qNgQXpuxmw9kwRJWxXvW36IcM5er+ZPbVz1kfvftqHgvBpuy2+uNM8N77w92IUc9FbK7L4CPE5EZjGK44nA7SKygLnh/VtVXY4bh+1EVZdE5MlArqr/GNadCkMZPwo8A/idsNv7gWuAD2ODBP5CVVVEjgK/XisMHgJeEdZ9KGx7c9j3fev6VM3YoRIjAS4G1thYUngo+qWuWSI9EGiNSGOUw2lrxbZxcyMtvDW/9Psw2wWPdRAOM7MYzXJbt9yz4uBggOyZD80xzm4Wwc5Tuh10aRlcWnXtRQmg+lLap8XWB84mMG8/GoojQFU/KiK3AB/HqIpPANcDn8HGld8W/mg/oqrPAx4IHBURD3wduLp2uJ8GbgRmgFvDC+BtwDtE5AtY5nxlOPdxEXkN5iUC8GpVPR7evwy4WUReG67pbdN+poX91zYtuHXUuwHLKSU1KVukL6BqXkmSkpMuqY213Pm0crZjmFn2vLgMofg3ycxITy3WmmjCvs6heV7xzOGmoUGCV29aua1oXOTOSeyyP98tbVRR1Vdicrc6Hr7Ctv8CPHKFdbcDj5qwvA/8xAr73IDx2uPLv4hJ79YNVbUBqQ0MXqFVa2smzvErIKupXEI7trTb1dDVuH8Nq9qCZlkViO87YX9nhTdlhGC8t6t1IeZ5kOh5c8Hr2GRskao5RHKbVZjMz5nCoyi4zf/J5nw3DXYkmgz6XMYOaRDZMUiSWuNHJS0rpWvjA1Fj4JyUNScJQjE6EzBAvbPqbrdT8s/lMSJFAuVUdShK2kT2zdk6V5O2hWBNmqKnFq2rr92GndRG32Br0ATocxP1UU8NaoiddMOsdK4j/oTSA0NVYTBY2UskFARHpmrXzqGFx999bzVUNfpyRP+NtAN5jmaZBea5mfL8GmRspbf0MENbKeIVf9c9uJmu+WXMz23d99Tg7EN3X6t3E6CnRBOcR7Fw4Dnl+/rMQjR4XNQ1z1H37E2lMd7SbIE5qy8IP7XiiXPrBnTaQWaj0X2C7plD2y1ztlMtf2orNZN/1fAzN+ldUHiASRbTiw6ivV7pJ9Lg3EWjg25w/mASXxyDczChB6qg5+vTTapMuWqxdqcH5vqoKQAt8MvL0B/YkIT9+8wHWsQCclYVDCUzNQaqlj2LjGpg47qZrtEnSWINMA3ObeyyIn9jndVgYxjLgiWa7KepBcT6H0LZLehPMwgacZIbGdaqK/8x+QK/uEjxjW8aZeI97tSSBeW8sKnYS714YTYTMQ4MiAE7XJd225aFz3bRuW0autDgrGEzzJJE5CEi8iER+Wwwg3vRVl1vk0E32DhqBbq6nE5E0Oj/HJtSxjHGMa8bQWInaQq9gXHPMXsPnYGaWJfjCHUR+fE0MT8P52A2RbLC3jc4d7F5TSg58POq+vEwUPtjInJb7NvYTDQBusHGUDfgjwgBWaPmOU47qRn4V0b2Nf30alA9nRuu2XPqMINsWErqNAnbR158uWeTwTutcLNQcFihMHFmTSqCqCKTbiQNzilsRpFQVb8BfCO8PyUin8U8fTY9QDcpQ4ONoW65GYNxbN+ueW5EaOgwBMpCYTn2ajWT+kmFu1pbuJ46ZdTJYGjn7fVDBi1I4dHlHsVdd1f8cy3rl94Aco8EzbYs9Vn49pee+XfTYMdC/HQv4AEicnvtdd3E45kH/fdgXc6bjiaDbrAxxC499YhL0WFmjSiquMRZe3ZsShnTNo8UCmHtwk1Yv1oTyZPTK+1aUjP/N4e4BGa6JHv3VLMTxQyKCAMBXF6YQ+CsSe2kN2DhouevaHLUYBdDWU+R8B5VvXy1DURkHvhT4OdUdUsGaTcBusHG4EPBrTbaSYdDm6pSblObrhIR7TXz/HRnuXHVRsA03X235aOt2Yfnr7GGlOAbLV5RJya5qxk5iar5eKQJtFL8N+8uR3c1OPewWTI7EWlhwfldqvrezTnq6Qpc6XYAABWESURBVGj+JzbYOFxs69ayxdrmBioUpmsWEXREQucrf+Vx1ALzmbZcR936oce8ygz8h3k5LFa9N0vRqDiJbeRfq1QhDc5RbEKADoM/3gZ8VlX/rzM/4spoAnSDdWPhgc8byZxppeZsF431fV41g6iWfDFQeiDbytMD4WZ7YRz72Kuq637ky9DU2tPVBWlgmiAzM7C4bH+7wSp14YHP2/55kw22FJvYqPJ4zMztUyLy92HZL6rqBzbl6DU0AbrB+lHPiAtvLdSxzhcKeGXDSMygKSbvX8NWGxUd+afXl+8XLrzO+PGBUS6aZVZYbKVolpsbXoNzC6qbYtivqn/N5OEfm44mQDdYP+rFPfUWe13V+VeaJdXkdSXEZu9pVmtIOQs4m0MXGpxF7K5GwiZAN9gAoiyuqA0v8AoaeOgkGBN5tUkq6kf9N7ya6ZG31nDUc1vxnu39DA3OSzReHA3OH5Tm/MGLOaofwhQTLYqRbHlk6rWvNZs0Lq4NtgPKZA+ZHYwmQDfYGOqTU2KBEIyTjsG59GeudRAWRQjKRbm8yZ4bbBt2V3xuAnQDOLznmWHiSYsjd//B2jv4WsobG1aiO1xZPKy2qdq7tZLZhRbuY8MmODfYPjQUR4PdhzhpG1i46PmUw18TV3lu5HnFPceCYMycY9u3YANcVat5gyucb+RngwbbhM1QcWwnGi+O8xyHOk+f3qh+XBpXK/zF7sAY0EVMslbtevooq2b+X4Ntha7jtUPQZNDnOY4N3rXmNgsHn2tv6qZGcUisajXSqj60NcLJiAR6Gl+NBg22AtaosoOi7xRoAnSDNTHOSy9c/IJAiTjzsYj8czQkimhaphvsNOyy/5JNgG6wbkidEkmSIKurgrN02uggDIfN8glHaNDg7GC3ZdANB91g3dDCG6XRadcc6Hy5rmxWidvvssJMg3MUDQfd4LxB1Dd3uzYVG9CYLee5BfEsPyueGw0aTMbmeHFsJ5oA3WD9CBO5y2JgmppyI/4eg3fi0HyXkX4Nzm3sMoqjCdAN1g813w11gmD2ndRHSiXOxmqOQxxNX3eDswbdnJmE24kmQDdYP2qPieoEmZ812i4blpNWJE1tWeERV/1VKMn40Ro02D7ssgy6KRI22DAkL8A5tJUiczNGecTOw6JAkjDeqqafHh9N1aDBtqIpEjY45xEVGmMdiDIzYwXDCSxGo+RosBMgu0yb3wToButHGPyqaWLDVlUti1ZF5ufQxSVIxVq/y10qB7sGDc4KlKZRpcF5ACemgQYzVQqGSdpKLat29VZvB0WYSbjL+L8G5xYEbRpVGuxMHN73bA7PXr1px9PlPlL4qp1b1YKxCJImpV90nJgySQ/doMG2Q3W61xoQkRtE5C4R+fRWXm6TQZ8HOLznmYBN2I7vAY6eunFjB8zzyn8D7H2a2M/CozFoJwmSJA210WDnYPMy6BuB3wX+cLMOOAlbGqBF5MXAczD251PAs4DXAD8MDIH/ATxLVe8XkRbwVuB7w3X9oar+53Ccx2BfyAzwAeBFqqoi0sG+oMcA9wL/QVX/JexzDfDL4VJeq6o3heUPA24GDgAfB65W1eEWfg1nHeOBOBr0H97zTDTPrcEkBFwtCo713rn6Ab1CUgVoTZ1xe868DmSmC4MhmvfKDFuSpCkUNji72EQOWlX/SkQeujlHWxlbRnGIyCXAzwKXq+qjgAS4ErgNeJSqXgb8M/CKsMtPAB1V/S4s4D639gX8PnAd8IjwWgjLrwXuU9WHA28CXh/OfQB4JfB9wGOBV4rIBWGf1wNvUtVHAPeFY5xXOHrqRo6euIGjp26cGIwPdZ5eviYiccjcrFEagdYAkEKrZXXD/iC/Gxkc26DBWYB4P9ULeICI3F57XXc2rnerKY4UmBGRDJgF7lDVY7X1HwF+PLxXYE5EUixTHgInReRiYK+qfhhARP4Q+BHgVuCpwKvC/rcAvytmtXYYuE1Vj4d9bgMWRORm4AnAfwz73BT2//1N/ty7CkcXbwKYyFEfnr8GSdPSqe7I8bcajdEfwIF9AEhvWKk5kgQBKyLmuTWtRIojaZpUGpxNrKtQfY+qXr6VVzMNtixAq+rXReSNwFeAHnBsLDgDPBv44/D+FizgfgML5i9W1eMicjnwtdo+XwMuCe8vAb4azpeLyAngwvrysX0uBO5X1XzCsc57HF1+x8jvI9NWvBnyLxx4DmCWoyqCOmf2ozGLznKjNbxWrnZxPFZTKGxwNqHsOiXRlgXoQCk8FXgYcD/wJyJylaq+M6z/JcyxIY70eCzW4vAg4ALg/xOR/4YNQhhH/JZXWrfe5ZOu/zqMVuHSSy+dtMk5j/q0lYX915ZBGkCzzLLjMPxVEwEEKTyaJpWcKQRqYHQiS4MGZwO7LEfYyr+YJwFfUtW7VTUD3gt8P5QFvB8Cnq5a3tL+I3BEVTNVvQv4GyBmzw+uHffBwB3h/deAh4RjpsA+4Hh9+dg+9wD7w7bjxxqBql6vqper6uUHDx7c4Fdw7uDI/W+z1/G3WqCNWucyENtLO6lZOuaB1milTebcYMdAQlKx1mvN44i8G/gw8EgR+ZqIbEktaysD9FeAx4nIbOCFnwh8VkQWgJcBT1HV5bHtnyCGOeBxwOdU9RvAKRF5XDjOM4D3hX3eD1wT3v848Bch4B8FDonIBSGTPwQcDes+RMV7X1M7VoNp4QR8MVogzHIrrnisw7BOjQCa5+bR0aDB2cQm6aBV9WmqerGqtlT1war6tq243K3koD8qIrdgUrYc+ARwPfAZoAPcFkYnfURVnwf8HvB24NMYFfF2Vf1kONxPU8nsbg0vgLcB7xCRL2CZ85Xh3MdF5DXA34XtXh0LhtjN4WYReW24pi35Ys9phMGwmgiSe7SdmnFS4atxWK0Ussy8orOhFRWThuJocBahap2vuwhbquJQ1Vdicrc6Hr7CtouY1G7SutuBR01Y3l9lnxuAGyYs/yLGdzc4E7jEnOwcyDA3L44stxmEqU35ljRFh5ltrx7VRmbX4CyjKRI2OC/gC1NrOEClcgmLHYYxk3am4HCzs/he/6xdboMGwK4L0M0zZ4P1ozZBRbKiCshpbeRV9OYAU3moIq10U/1AGjRYF5Tq/+5arx2CJoNusH4EqZ30M8uiO21UxFq9c8B7M1KK2UqSIO0WZGLFwgYNzgp01ymKmgDdYP0IemYZZmiwHdVWMmKGrolDonIjen0k1m0YDZs2bNbUoMFGoDRFwgbnAf5ne+cfY0dVxfHPd+a9/dEfQFvAAC0gKEQQRalEBRQ1UFYJaAQlQSX2D4OgIEoIhgSMaBQwQQ0SAv4hBhAQBQOG8sNQf0QUaCgtvylQxED4DQW73ffezPGPe2ff7LJbXrvbfbO755PczJ377p058/Lm7Nlz7z2nCCX66utou/nkC+ejPMeSBPrqJG8OhsnCwh9dTBSmCUp7huN0LNt+efRjJ9z2xtvmcx1n8nEftDPTsWYLy7Lgroi+ZqslkMTIdsVLUPimiyBJZsH6LqXKskZwkxy1w6yLWeV0g0laBz1VuAXtbDnxB6w5/ZBlw0pZmYX4HH096LUNWKGQyQFrb1QpNrekSVgbnWUYhDgfSljxyuVdeSxnplMt5dsJrqCdrUZ9fSF7dzPDkgSTwlbZNzdiWSmLSpoG319uoBhLOhE048tSr4djbpAEa9qyzH3UzuRitDMATRNcQTtbR55jQ422Ek5ArRy18mAV51k7uFKWhR2FRd/y6o6+3hHLmixO4lirxZH9X0FpOhwO1XEmjFvQzoynsEKaDVBC8soG8kXbhTYzGGqEnYbNISANFnOeBXdHloW2wi+tBMiwRkxqkyRYliEpKG/HmTR8q7czC1BPzOgdXRK0WiEmRy1OAEowNBR80I1GULQSYGHDCsTJw3i0nLzRRDGHYdLbG2J5SKheZ2DJ6di8Oax45Cfde2hn+mNgvg7amekUEWJVq9Hcfw8A0qGwo1CtHBscDKs8hobCgCxD8+aGZXaWtK3nGLJUfX2k9Z6wmaWnjtVrwUpP02CNDzXQhrcY2PvMEGs6boK59cmfTfmzO9OcCu0S7ARfZudsOXnbT1x/YQPKjLyWYImCFd3bG6zhWi0o2SQJ/1oqCSUvzaYnpRUdtXaMacU+1tvTznO4aShEzTODZouBXb/Vjad3pjPTbJmdK2hny7GwZE4x/oaynKSVo+J3XUoWm/T3od7eoIgtD5OFaQj4r3RkrA6i8tVwBLxYr6XBd10OwlSvQb3OUfucNaWP7kxjzGI6tg5KRXAF7UyMJCHvrbV//BB8zolQzOZNTz0o5hHjouUsYTH7SgjqH1+OPA/KubDWzcLnGwfb2VoADTUZ2PtMBvY4Yxs/qDMjcAvamTX01Gku6Ee5BfdGWtpFqLZ1PMxwW3BzmIVSbB0nt5AJvLDAi7HFGuoY08MajbB9vNmM66lDotqBJadPzXM70xQLcyMdlKrgk4TO1iGFDCpFSqs0QWbkc3pINiQxQH/M71Z2TygZdm3Y6CVPibA8D0GWLA07EoeCu0O1tB2+tJWFLeKtVlgRUouTimZBSScJtz5z8RR9Ec60oQg3Oo1wC9rZcgrLNk1IBtvhQy0NuwmtXkN9fcFH3WyNufZ02HIe3rySB0VeTCBmGdrUiL5rC24SgCQq+Dwb/iNQbCE3s6C4zRjY63vb9CtwpinFb+6dSkVwC9oZk2XzTirFzEixVmv4X7/hddA7LyLvr2GJkIEJ0kYp3nNvb8hH2GoFZZ2mkAR3CBDO83aMXtVqQclGhS4zrBlSZ9GKSWrL/sHe3hDHo9EME4/xM/vfxrf7vJ1ZjwE2zSxo/xU7ABxROwEAxRRVKhRcEtctl33JhMk9zIJrIzeSRjacRFYbN4Vt4JS2bpshyyFP2lvAYYS1YlbKZlHOYVjMqtdSyIKrRD09YXKxsM4LF0qREKBYg+04BWaVso47wRX0LGbZnK+OcBFAsDCUMnJJWx5Cit4+eBXAcGhQbdxE/WXI5/SgZoaSJKTAyqL7oVkK2E+wYNTXE1+SdMR92+ujSy9QMbYsSy1tx5nOY9yPeP3hCcZk5B8Txymo0gRgJ8gqtKSkqkh6CXim23KMYkfg5W4L0SEu67ZhNsm6h5ntNBEBJK2IcnTCy2Z21ETuNxm4gp6mSLrPzJZ2W45OcFm3DS7rzMdXcTiO41QUV9CO4zgVxRX09GU65YVyWbcNLusMx33QjuM4FcUtaMdxnIriCtpxHKeiuILuApJSSfdLuiWef1DS3ZLWSrpZ0nalvt+XtE7SY5KWldoPiv3XSfqlpJhJSr2Srovt/5a0Z2nMSZKeiOWkDmVdH++zWtJ9sW2hpDvide6QtKAK8o4j6/GSHpKUS1o6qn/VZL1I0qOS1ki6UdIOFZb1/Cjnakm3S9q1CrLOOIqQj16mrgDfBa4Bbonn9wKfjPXlwPmxvh/wANALvBt4EkjjZ/cAHwME3AoMxPZTgMti/QTgulhfCDwVjwtifUEHsq4HdhzVdiFwdqyfDVxQBXnHkfV9wL7ASmBpqb2Ksh4J1GL9gop/r9uV6qeV7tX13+xMKm5BTzGSFgOfA35dat4X+Fus3wF8MdaPBa41syEzexpYBxwsaRfCC3K3hV/yb4HPl8ZcGes3AJ+Jlsoy4A4ze9XMXov32dqdUuV7XDnq3pWS18weMbPHxnmGqsl6u5kV0ab+BSyusKwbSqdzCTv5KynrdMYV9NTzc+AsoBy15UHgmFg/HlgS67sBz5b6/Te27Rbro9tHjIkv+xvAos1c650w4HZJqyR9I7a9y8yej/d4Hti5IvKOJet4VF3W5QQrs7KySvqxpGeBE4FzKyLrjMIV9BQi6WjgRTNbNeqj5cCpklYB84FGMWSMy9hm2rd2zOY4xMw+DAxEGT+xmb7dlndGyCrpHKAFXF1lWc3sHDNbEuUsMvh2W9YZhSvoqeUQ4BhJ64FrgU9LusrMHjWzI83sIOB3BL8dBIthSWn8YuC52L54jPYRYyTVgO2BVzdzrc1iZs/F44vAjcDBwAvxX1bi8cUqyDuOrONRSVnjRNjRwInRFVBZWUtcQ9st1/Xf7Iyi207w2VqAw2lPEu4cjwnBN7c8nu/PyAmXp2hPuNwLfJT2hMtnY/upjJxwuT7WFwJPEyZbFsT6wneQcS4wv1T/J8EHeBEjJwkv7La848la+nwlIycJKydrLA8DO43qX0VZ31vq823ghm7LOhNL1wWYrYWRCvp04PFYfkrc4Rk/O4dgUT9GnPWO7UsJvusngUuKMUAf8HvC5Mw9wF6lMctj+zrg6x3IuFd82R4AHgLOie2LgL8AT8Tjwm7LuxlZv0CwxIaAF4DbKizrOoLPdXUsl1VY1j/E+64BbgZ267asM7H4Vm/HcZyK4j5ox3GciuIK2nEcp6K4gnYcx6korqAdx3Eqiitox3GciuIK2ukKkrIYCa0oZ2/FNQ6X9PHS+cmSvjaJMt4gaa8t6H+ApN9M1v0dp9ZtAZxZy6CZHTjBaxwOvEXYPIGZXTZRoQok7U/YYPFUp2PMbK2kxZJ2N7P/TJYszuzFLWinUkg6V9K9kh6UdHkpZvBpkh6OMYivjTGDTwbOiBb4YZJ+IOnM2H+lpAsk3SPpcUmHxfY5kq6P17kuxh9eOoYoJwJ/Ksn1VrzeKkl3Sjo43uMpSceUxt1M2A3nOBPGFbTTLfpHuTi+HNsvMbOPmNn7gX5CXAoIW8o/ZGYfAE42s/XAZcDFZnagmf19jHvUzOxg4DvAebHtFOC1eJ3zgYPGke8QoBzUai6w0kK8lDeBHwFHEHYq/rDU7z7gsA6/A8fZLO7icLrFeC6OT0k6C5hDiMXwEMEqXQNcLekm4KYO7/HHeFwF7BnrhwK/ADCzByWtGWfsLsBLpfMGsCLW1wJDZtaUtLZ0bQiBo3bFcSYBt6CdyiCpD7gUOM7MDgCuIMRpgJDk4FcEi3dVjHr2TgzFY0bbGBkrhOVYDJbuDdC0dlyEvLi2meWMNHT64ljHmTCuoJ0qUSjElyXNA44DkJQAS8zsLkKygx2AeQRXw/wtvMc/gC/F6+4HHDBOv0eA92zhtQH2IQQEcpwJ4y4Op1v0S1pdOl9hZmdLuoLgQlhPCE8JkAJXSdqeYAFfbGavS7oZuEHSsYSQl51wKXBldG3cT3CdvDFGvz8TVoncuWWPxafiWMeZMB7NzplVSEqBupltkrQ3IVzqPmbWGNWvH7iLkE0k6/DavcBfgUOtnVvQcbYat6Cd2cYc4C5JdYI1/s3RyhnAzAYlnUfIgdfpmubdCYkMXDk7k4Jb0I7jOBXFJwkdx3Eqiitox3GciuIK2nEcp6K4gnYcx6korqAdx3Eqyv8BG3x12dCOaO0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The final step is to apply the linear regression function\n",
    "\n",
    "gw_logEC = slope * np.log10(wt_ave_grid) + intercept\n",
    "\n",
    "\n",
    "gw_EC = 10**(gw_logEC)\n",
    "\n",
    "plt.imshow(gw_EC, extent= [minx, maxx, miny, maxy])\n",
    "plt.title('Predicted gorundwater EC for top ~20m\\n of saturated aquifer material')\n",
    "plt.xlabel('Easting (m)')\n",
    "plt.ylabel('Northing (m)')\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('EC (S/m)')\n",
    "\n",
    "\n",
    "outfile = r\"C:\\Users\\PCUser\\Desktop\\EK_data\\Interp\\EK_gw_EC_saturated_top_20m.png\"\n",
    "plt.savefig(outfile, dpi= 300)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the TDS with an empirical conversion factor\n",
    "\n",
    "conversion_factor = 0.709\n",
    "\n",
    "gw_TDS = gw_EC * conversion_factor\n",
    "\n",
    "gw_TDS = gw_TDS * 10000. # convert to mg/l\n",
    "\n",
    "plt.imshow(gw_TDS, extent= [minx, maxx, miny, maxy])\n",
    "plt.title('Predicted gorundwater TDS for top ~20m\\n of saturated aquifer material')\n",
    "plt.xlabel('Easting (m)')\n",
    "plt.ylabel('Northing (m)')\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Total dissolved solids (mG/L)')\n",
    "\n",
    "\n",
    "outfile = r\"C:\\Users\\PCUser\\Desktop\\EK_data\\Interp\\EK_gw_TDS_saturated_top_20m.png\"\n",
    "plt.savefig(outfile, dpi= 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the outfile\n",
    "\n",
    "null = -9999.\n",
    "\n",
    "EC_outfile =  r\"C:\\Users\\PCUser\\Desktop\\EK_data\\Interp\\EK_gw_EC_saturated_top_20m.tif\"\n",
    "\n",
    "new_dataset = rasterio.open(EC_outfile, 'w', driver='GTiff',\n",
    "                            height= gw_EC.shape[0], width=gw_EC.shape[1],\n",
    "                            count=1, dtype=np.float64,\n",
    "                            crs=wt_src.crs, transform=wt_src.transform,\n",
    "                            no_data = null)\n",
    "\n",
    "# set nan to no data value\n",
    "gw_EC[np.isnan(gw_EC)] = null\n",
    "\n",
    "new_dataset.write(gw_EC, 1)\n",
    "\n",
    "new_dataset.close()\n",
    "    \n",
    "\n",
    "\n",
    "TDS_outfile =  r\"C:\\Users\\PCUser\\Desktop\\EK_data\\Interp\\EK_gw_TDS_saturated_top_20m.tif\"\n",
    "    \n",
    "new_dataset = rasterio.open(TDS_outfile, 'w', driver='GTiff',\n",
    "                            height= gw_EC.shape[0], width=gw_EC.shape[1],\n",
    "                            count=1, dtype=np.float64,\n",
    "                            crs=wt_src.crs, transform=wt_src.transform)\n",
    "\n",
    "# set nan to no data value\n",
    "gw_TDS[np.isnan(gw_TDS)] = null\n",
    "\n",
    "new_dataset.write(gw_TDS, 1)\n",
    "\n",
    "new_dataset.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "today = datetime.datetime.now()\n",
    "# dd/mm/YY\n",
    "d1 = today.strftime(\"%d/%m/%Y %H:%M:%S\")\n",
    "\n",
    "# Write out a metadata file\n",
    "\n",
    "s = \"Salinity grids created at  \"\n",
    "s+= d1\n",
    "s+= \"\\n\\n\"\n",
    "\n",
    "s+= \"The EC grid \" + EC_outfile\n",
    "s+= \"\\nwas created using a linear the linear regression function \\n\"\n",
    "s+= 'log10 EC = ' + str(np.round(slope,3)) + ' * log10 sigma + ' + str(np.round(intercept,3)) \n",
    "s+= \" which had an r-squared value of \" + str(np.round(r_value**2,3)) + \".\\n\\n\"\n",
    "s+= \"The TDS grid \" + TDS_outfile\n",
    "s+= \"\\nwas calculated using the following formula \"\n",
    "s+= \"TDS = EC * \" + str(conversion_factor)\n",
    "s+= \".\\n\\n\"\n",
    "\n",
    "name = 'Neil Symington'\n",
    "s+= name\n",
    "\n",
    "# write the metadata file out \n",
    "\n",
    "outfile = r\"C:\\Users\\PCUser\\Desktop\\EK_data\\Interp\\EK_salinity_grid_metadata.txt\"\n",
    "\n",
    "with open(outfile, 'w') as f:\n",
    "    f.write(s)"
   ]
  }
 ],
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